As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage , availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment. Before the advent of cloud computing, data used to be stored and retrieved locally from a single machine, but the single machine was not reliable due to synchronization issues of the backup data. In case of damage, the users were not able to retrieve recent data from that local machine. Moreover, keeping the data secure in that single machine was another issue. The synchronization of the data in the distributed system was also crucial. Moreover, the limitations of storage for a single machine was another issue in case the volume of data exceeded the machine’s storage limit.Cloud computing is defined as a network of distributed computing on a large scale which computes through highly available, dynamically configurable/reconfigurable, and scalable resources. According to the definition of cloud computing offered by the National Institute of Standard and Technology (NIST) , it is aOver time, it has been observed that the amount of data is increasing exponentially. Large and huge datasets of scientific domains are also becoming much important to the shared resources. Usually, these kinds of huge and massive data or datasets are stored in data centers residing in cloud. As most of the scientific, as well as organization’s and industry’s data, is in large volume, i.e., in Terabytes (TB) or Petabytes (PB) , the spaDistributed storage assumes a significant function in the current computerized time because of the noteworthy headway of medical care advancements. As the wellsprings of cloud storage worried in healthcare organizations and different segments are notable for their volume and decent variety, thus, the healthcare services are picked up its impact through the effect of cloud storage. The healthcare organizations have produced a huge measure of medical services information over recent years. Medical services information for the most part consolidates medical records electronically known as EMRs, for example, keeping the history of patient’s medical health and clinical data, doctor notes, reports generated at the clinical centers, biometric information, and other clinical information identified with healthcare systems . All thiThe availability of data is the main concern in cloud storage. By availability, it is meant that the data should be available and accessible whenever it is requested or needed by the users. As the volume of data is increasing every day, the availability and security of data are becoming of more concern to the cloud storage providers. Therefore, more efficient methods and optimization techniques are needed as simply increasing the replicas or copies of data will also demand the increment in the storage space and cost factor. However, keeping data available at full time in cloud storage is much more necessary; otherwise, immeasurable loss could occur.The domain knowledge and literature show that enormous research is present and is accounted for by numerous scientists and researchers around the globe for healthcare and medical services in cloud storage. This exploration centers around separating noteworthy highlights of cloud storage in the healthcare medical services uses of medical care in cloud storage, and cutting-edge methods proposed in the cloud storage and medical services cloud storage field which can, at last, be utilized for dynamic in health and medical care. The present work sorts and sums up the current distributed exploration work dependent on the formulated research questions and identified keywords recognized for the searching cycle. The analysis in the present research work will support the specialists, clinical specialists, general doctors, and professionals to make more reliable conclusions, which eventually will assist with utilizing the investigation as proof for treating patients and propose medicines likewise.Based on the criterion defined, i.e., inclusion, exclusion, and quality assessment, a total of 90 most primary applicable studies were included in this paper. The main contributions made are as follows.To highlight the research work in the field of cloud storage mechanisms and its impact on healthcare environment did from the year 2007 till 2020To briefly present a summary of the techniques used for cloud storage and healthcare in cloud storageTo highlight the benefits of healthcare in the field of cloud storageThe paper is organized as follows. The conducting of a detailed process of research with a systematic literature review based on guidelines is presented in The following objectives are focused on the research process for conducting a Strategic Systematic Literature Review (SSLR) in this paper.Evaluating diverse aspects on the idea of distributed cloud storage in the health and medical care context.Exploring the sources of health and medical care in distributed cloud storageInducing concentration to overcome the challenges of cloud storage in health and medical careThrough the in-depth discussion of these goals, the SSLR aims to help with understanding the general effect of distributed cloud storage and its applications in the health and medical care domain.The main Research Questions (RQ) that are formulated to address and conduct the SSLR of the proposed study are as follows.RQ1. What are the features of cloud storage in the medical healthcare field?RQ2. What are the challenges and opportunities of healthcare in cloud storage?RQ3. What methods and frameworks are used for cloud storage in healthcare systems?RQ4. What are the applications of cloud storage in healthcare?RQ5. What research has been published in cloud storage mechanisms and healthcare cloud storage since 2007?To look for the acquisition of relevant articles for our SSLR, we have used six main electronic research repositories: IEEE Xplore, ACM, Taylor and Francis, Science Direct, Wiley Online Library, and Springer. However, some of the works published by MDPI and Hindawi, which are fairly relevant to our domain, are also included in this study.For the searching process, we defined the following keywords; “cloud availability”, “cloud storage”, “healthcare cloud storage”, and “e-Healthcare cloud storage” in the context of the research domain. From the table of content for these keywords, we carried out an automatic in-depth text search by search engine screening and manual screening. To conduct an SSLR, Boolean operators were used with the predefined keywords and within the scope of our formulated research questions to classify relevant articles. We applied a specified time duration limit on our search according to the inclusion and exclusion criteria, so that all relevant papers should be identified and collected having a focus on the predefined keywords. To make the identified research papers to a manageable and reduced size, the paragraphs of these papers were further scanned out with the keywords.Based on the following inclusion–exclusion criteria, we screened, filtered, and sorted the article in this paper: Inclusion CriteriaThe articles relevancy to the cloud storage mechanisms and healthcare in cloud storageThe articles published during the years 2007–2020English should be the main and primary languageSelection of only primary studies from the relevant researchThe article should provide a sound knowledge from the research questions formulatedExclusion CriteriaThe articles before 2007Less than three pages of research articlesGray PapersDuplicate version papersProvide no information for the research question formulatedWe performed the SSLR process in four stages. The outcome of the in-depth reading and analysis resulted in the following schematic classification and dimensionality; (1) theme of Availability in Cloud Storage, (2) purpose of application, (3) level and characteristics of Availability in cloud storage, (4) mechanisms of availability, and (5) finally the reliability in cloud storage.Quality assessment plays an important and essential role in the SSLR protocol during the review process. All the articles were quality assessed, by all authors, after the analysis and evaluation of abstracts of selected articles. During the reviewing and screening process, quality valuation assumes a huge part in the SSLR procedure. The investigation and assessment of the abstract of selected articles were performed by all the authors to evaluate the quality of finalized and screened articles. These articles, based on the inclusion–exclusion criteria, were selected for each of the formulated research questions.During the reviewing and screening process of SSLR in the present research article, articles were collected based on predefined searching keywords through a search operation on the six most common electronic repositories: ACM, IEEE Xplore, Science Direct, Springer, Taylor and Francis and Wiley. In the preliminary phase, approximately 5163 articles were collected for the years 2007–2020. In the next phase, screening on the basis of title and keywords filtered out total of a 1430 articles. In the final phase of the SSLR process, the Boolean AND operator were used within the predefined searching keywords to filter the remaining article based on the abstract. The resulting 90 articles, according to each defined research question, were finalized and selected for further study and analysis by the authors according to the inclusion–exclusion criteria.A scientometric analysis, similar to the one as represented by Sabine et al. In , is alsoAlthough the healthcare industry enjoys valuable benefits from the cloud-based healthcare architecture, but unfortunately, the major challenges of healthcare technology and cloud computing together remain in existence which adds more weight to requirements and encounters for storing and processing sensitive medical data. In this section, we summarize some of the technical and non-technical requirements and challenges faced by the healthcare cloud.Availability: Most clinical consideration providers require high availability and accessibility of cloud-based health and medical care organizations. Availability and data accessibility are basic requirements for clinical administration providers who cannot effectively work unless their applications and patients’ data are not available. The cloud-based health and medical care organizations should be available reliably with no impedance or data corruption. Cloud organizations could experience frustrations due to programming and gear inadequacies, arrange disappointments, security dangers, and disastrous occasions among various reasons. As CSC assets are scattered over an open cloud infrastructure, for instance, the Internet, they will not offer better accessibility stood out from guaranteeing and keeping up IT structures inside the affiliation.Reliability of Data and Service: Using the cloud for a noteworthy application like cloud-based health and medical care require affirmations of good reliability for the offered kinds of administrations. All cloud-based medical services organizations and data must be with no failures. Some critical decisions regarding single human or society healthcare administrations can be taken depending upon the data and organizations given by the cloud-based health and medical care. As such organizations are passed on and may start from different Cloud providers, the chance of having imperfect or wrong data, or organizations, can increase. The data in cloud-based health and medical care must be dependable and ceaselessly in a legitimate state paying little intellect to any item, product, or framework failures. Additionally, all cloud-based health and medical care must pass on fault-tolerant organizations for clinical administration providers.Data Management: Huge amounts of clinical and medical records and images related to an enormous number of people will be taken care of in cloud-based medical care. The data may be replicated for high trustworthiness and better access to different regions and across tremendous geographic divisions. A segment of the data could be moreover made open locally. Most health and medical applications require secure, gainful, strong, and adaptable permission to the clinical records. These necessities actualize the need to have some cloud storage organizations that offer transformation to intellectual faults, secure limit over open fogs, and rich inquiry accents that grant viable and flexible workplaces to recover and deal with the application data.Flexibility and Scalability: A cloud-based healthcare system must be equipped for serving various health and medical care providers with various necessities. These prerequisites are as far as capacities, activities, clients, evaluating, the board, and quality of services (QoS) requirements. The cloud-based medical care frameworks and administrations should be adaptable enough to be arranged for various medical services providers’ necessities. Similarly, the e-Health Cloud should be genuinely versatile in adding new expected organizations to help clinical administration measures. While e-Health Cloud organizations must be versatile to meet differing clinical administration essentials, they moreover ought to be viably configurable to meet with different needs prerequisites. Therefore, the structure of cloud administrations to meet different requirements must be cultivated with the least effort and cost.Maintainability: Dissimilar to having an e-Healthcare framework for singular medical care specialists and providers, a cloud-based healthcare system can be utilized for many medical care organizations. This expands the multifaceted nature of framework practicality in the cloud-based healthcare contrasted with a singular healthcare framework. The extension is for the most part a result of the need to consider the requirements and characteristics of the diverse healthcare organizations providers and clients. These necessities can be uncommon while up-keeping in the cloud systems, programming, or stages must be overseen without having any negative consequences for any organization obliged with any client.Change in an Organization: The shifting from in-house towards cloud-based healthcare infrastructure will require huge changes to clinical, medical, and business communities and to the authoritative limits in the medical services industry. This analysis is involved with the progressions that a cloud-based healthcare will present upon members.Data Ownership: Responsibility in the medical care industry by and large is a territory with no evident rules and regulations. Can a doctor guarantee possession for the patient’s records which could be the sole property of the patient? Should there be some back-up plan for the patient’s back-up plan or the emergency clinic executives? This challenge is involved in the development of organizations and policies that draw clear ownership limits.Issue with Liability, Trust, and Privacy: This requirement and challenge relates to the prospects of exposure to private and sensitive information, information loss, and the dearth of information about the area and purview of the clinical information. From the point of medical services providers’, cloud-based healthcare presents a high exposure of commitment and legal liabilities in instances of information loss or threat that may cause harm to reputation and patients’ trust.Consumer Usability Experience: This requirement and difficulty regards the degree and level of acquisition received by the cloud-based healthcare clients including patients, medical services experts, and regulatory and protection faculty.There are four main basic factors of cloud storage availability that needs to be considered when talking about cloud data storage. A general overview of these factors is depicted in “vendor lock-in” problem. These vendor lock-in problems can be resolved through the use of a multi-cloud storage system in which user’s data is not only bound to one storage provider but rather it is distributed over available multiple clouds. This also benefits the user in the reduction of user’s data storage and the increase in the availability of data over the cloud.Features like data distribution over geo-locations, specific Application Program Interfaces (APIs), etc. are some that are being offered by most of today’s Cloud Storage Providers. Users may move from one provider to another if the new provider offers services that are more attractive and cost-effective or the old providers may increase the pricing of its offered services. Somehow, the switching cost of moving from one provider to another may be very high  as it isThe transmission of data over the network through some medium is known as bandwidth or sometimes referred to as throughput. Subjected to the other factors like latency, the performance could be better if the bandwidth capacity of the network is higher. Bandwidth demand has been increased in the last couple of years due to the reason that both the industrial and end-users have started relying more on the cloud storage instead of their in-house software and hardware. Due to which costs of bandwidth are increasing likewise other resources on the cloud . RegardlThe total available bandwidth and the bandwidth utilized by the currently running application for a cloud user is the calculation criterion used by the Cloud Service Providers (CSPs) for customer bandwidth. Similarly, the bandwidth is optimized over the internet through online backups and several ways by CSPs. These ways used by CSPs keep on reducing the data been flowed through the internet bandwidth tunnels and techniques like deduplication and compressions over the cloud . In thisThe emergence of a newer cloud provider with a lower pricing scheme and more advance features remains an increasingly common occurrence in the cloud environment. This puts a cloud user in a decision-making state, either to transfer and migrate all of their data to the new cloud or remain with the existing one. Some may incur a high switching cost and migrate to other newer cloud while others may remain with the old ones and save the high cost of switching. Switching time from one cloud to another greatly affects the cost factor as switching earlier could be worse in some cases and switching later could have lost its value. Therefore, a user can benefit and take advantage of newer and old providers with best performances at a lower price through the distribution of their data over several cloud providers, which will entail only a fraction of data to be switched.All the pricing in the cloud storage is based on a flat rate of storage and a usage rate of the network. Therefore, the operations that are being performed or requested for the data stored over cloud storage play an important role in this regard. There are four main types of operation performed in cloud storage: POST, GET, PUT, and DELETE.As it is already mentioned, the cloud storage providers choose their pricing according to the above-discussed operations; therefore, the selection of storage provider based on the pricing schemes of these operations play an important role.Most of the issues with the cloud storage data centers occur when they try to run heterogeneous multiple tasks and achieve high-level reliability at the same time . The incAccording to a survey  in 2012,Similarly, most data loss occurs when the customers outsource their data to only a single cloud storage provider and lose control of their data. Therefore, whenever there is a failure to that single provider, then the user will not be able to access their data. Cloud of clouds is one of the solutions suggested in ,21, whicA report by the authors of  shows thKeeping all of the above-mentioned outages issues and problems in mind, it is obvious that the user cannot be guaranteed long-term availability  for theiOutage Cases:On the 24 of February 2010, the Google app engine suffered from an outage that lasted for almost two hours . AccordiMajor online encyclopedia “Wikipedia” also suffered from a major outage on 24 of March 2010 which lasted many hours . The reaOne of the most used online peer-to-peer (p2p) communication service “Skype” also suffered from an outage of about 24 h from 22 to 23 of December 2010 . The cauOne of the Amazon web services (AWSs) known as Elastic Compute Cloud (EC2) also suffered major outages and failures twice in 2011 ,30, whicIn February 2017 , the AmaTherefore, achieving high availability is an ongoing problem in cloud storage . MoreoveCloud-of-Clouds or Multi-Cloud are the terms that have been proposed by researchers to protect against the problems that were prompted in single cloud storage. These multi-cloud storage solutions provide so much agility to the user that they can assure their data is available all the time against the outages occurring in a single storage provider. The mobility of these multi-cloud storage systems is also observed in their redundant distribution over multi Geo-location through their redundancy mechanism. A multi-cloud solution also provides durability to data, fault tolerance, and avoidance from vendor lock-in problem through data stripping and distribution over multiple locations . In suchAccording to the report conducted by 451 Research LLC on 1734 companies , one of It is understood from all the above discussion and cases that the outages that could last long are inevitable. Therefore, to make our cloud storage architecture tolerance free from the causes of outages, the following are the basic mechanisms used for the minimization of cloud outages and an increase in the availability of data over the cloud, whereas N copies of the data, where N > 1 always. These N copies or replicas make the data available all the time within the same cloud or on multiple geographic clouds. In the case where one of the clouds fails to provide the required replica due to any reason, then the replica from the other cloud is benefited to its user. This also ensures that the data is always in synchronization with the original data and with other geo-clouds. As it is unlikely that multiple clouds suffer from outages at the same time, the Inter-cloud replication is very effective in this manner [Replication is a mechanism that works by creating multiple copies of original data and storing these copies or replications either within the same cloud, on geographical clouds, or both. Through the use of replication there is an increase in the availability of data, while, on the other hand, there is a reduction in the time latency of user data requests. Replication also reduces the bandwidth consumption made by the copies or replicas of the same data or service. This mechanism works by creating s manner ,38. The s manner ,39 intros manner  replicats manner  have pros manner  have pros manner .(A)Replication Types There are two main categories of replication schemes, i.e., static replication and dynamic replication [lication . Static lication . In statlication . Moreovelication , MinCopylication , RFH [44lication , etc. arlication , CDRM [4lication , CIR [48lication , etc. arlication .(B)Replica/Data Selection In the replication scheme, the availability of cloud data depends on the basic three operations of a cloud storage system, i.e., which data or replica to be selected, where the replica/data is to be placed, and finally when the replica/data is to be placed. Initially, the replica selection plays an important role because the right selection of replica may reduce the waiting and response time of the requested data by the user while increasing the availability [lability . Moreovelability  have uselability  availabilability  have pro(C)Replica/Data Placement As the number of data chunks or replicas in the replication mechanism is increasing for the achievement of high availability and reduced waiting time, the management of these replicas is also becoming critical accordingly. Moreover, increasing the number of replicas after some threshold will not necessarily increase the availability but will become the overhead. Therefore, in these scenarios, the location where the placement of replicas should be done becomes an important open issue that needs to be solved. However, the issue becomes more complicated when the replicas are to be placed in virtual data centers which are dynamically ultra-large and scalable [scalable ,52.Due to the socially-aware nature of the cloud, it becomes more challenging for the multi-clouds to achieve data/replica placement more conveniently. This type of socially-aware network over clouds requires that all related or friend’s data should be placed on clouds close to the user, so as to avoid the latency and availability issues. The authors of  achieve The authors of  provide Many cloud storage providers use the Associated Data Placement (ADP) model proposed in . The autn no. of blocks. The equation “n = k + m” satisfies this statement. Here, “k” is the original amount of data, whereas “m” is the redundant or extra data that is added to the original data for protection from failures. Here, “n” denotes the total data that needs to be stored over cloud storage after erasure coding process. Next, the encoded blocks are generated through “n-k” of “k” block, where “n” is the total number of blocks and “n > m”. All of these “n” blocks are then uploaded to exactly “n” no. of cloud storage where each cloud storage will be storing only one data block respectively. Making data available all the time through the elimination of corrupted data and reconstruction of original data by using the information stored elsewhere in the same cloud or in Geo-locations multi-cloud is the main goal of the Erasure Coding mechanism. Moreover, erasure coding also provides the reduction of total amount of data that is transferred over cloud storage. The general workflow of erasure coding is shown in Erasure coding is another availability improvement mechanism compared to replication used in the cloud storage systems. Despite the availability enhancements, it also reduces the cost of data storage over the cloud. The Basic working mechanism of erasure coding involves the breaking and dividing of data into There are many techniques ,16,57,58The authors of  propose Data deduplication is a mechanism through which redundant data in cloud storage is eliminated and storage space is maximized. Basically, only the original data or one instance of redundant data remains on a single cloud storage media, whereas other multi-clouds only have the pointer referring to that original data or instance of redundant data.The general workflow of data deduplication is depicted in Data deduplication has been categorized into two levels, i.e., file-level deduplication and block-level deduplication, in ,67, respThe models in ,68,69 prAs mentioned above, for large volume of datasets, i.e., terabytes to petabytes, it becomes a bottleneck problem for the deduplication mechanism to store the hash values of each data chunk or block of such volume in memory. To overcome such a problem of storing hash values of huge data, the authors of  have proMoreover, the distribution of total number of papers included in our literature review and analysis is depicted in As switching between cloud providers in a multi-cloud environment is very expensive for the customers due to the lock-in problem, it becomes beneficial and success for some cloud providers. The authors of  present Similar to the RACS in , the autThe authors of  proposedThe authors of  proposedThe authors of  have pro VPS.NET  have beeThe authors of  proposedThe authors of  have proAs most of the time users store their data on single cloud storage, it puts them to a high risk of loss if that storage provider suffers from an outage or it raises the prices of its storage space, operations, etc. Keeping these types of issues in concern, the authors of  have proIn , the autThe authors of  have proSyncopy  lets theThe authors of  have proThere are two types of tasks when talking about storage systems, i.e., high priority tasks and low priority tasks. High priority tasks are run in the foreground, while low priority tasks are run in the background. The authors of  have proThe authors of  have proIt is promising for the medical care organizations to consider receiving healthcare data innovation frameworks because of the ascent in the expenses of medical care administrations, medical services experts are becoming limited and difficult. To provide more effective and efficient medical and health services, HIT allows healthcare institutes and organizations for streamlining many of their processes. For making true enabling of HIT services over the internet and cloud, it makes use of latest technological trends like Cloud Storage Computing (CSC) infrastructure. In the following subsections we have discussed and highlighted some of the constituents and proposing the building of cloud storage over the e-Healthcare environment.There is a huge volume of information in social insurance that is identified with various medicinal services areas particularly neuro and heart. This information needs an extraordinary concentration and the models right now concentrating on these areas needs to actualize the most recent advances to foresee a few examples. The authors of ,91,92,93Blending the Internet of Things (IoT) with building data displaying can improve the presentation of the information assortment. The authors of  have utiThe work in  distinguThere are many techniques available for the enhancement and optimization of cloud storage availability, but The overall purpose of all the techniques mentioned in Following are some of the limitations to the validity of our proposed work.The work presented in this paper through the SSLR process is limited to only six most extensively used repositories, whereas there exist number of digital repositories for accumulating the research articles.There are continuous publications in the related domain on daily basis, but our current work only follows the research done in years ranging from 2007 to 2020.There may be articles, that have been skipped, while they contained the keywords healthcare and cloud storage but for the implementation purposes they have no concern.Fundamental purpose of skipping Google Scholar in our research is to save time from getting difficulty in the duplicate entries.A large number of research has been published in the related domain but the authors limited their research through their defined set of keywords. More keywords can be defined to enlarge the work in the future.There are basic problems that each medical consideration affiliation must suffer when adapting to cloud-based services. Turning over data, security, availability, and control to an outcast suggest that your association has unquestionably no impact over where its data truly lives. Trust in your cloud dealer takes on a whole new significance. Security and insurance, which are key issues in the clinical consideration industry, must be invulnerable.Changing from an on-premises foundation to the cloud suggests changing your entire methodology for dealing with tasks. Clinical administrations providers needing to realize a cloud game plan must ensure that everybody comes up to speed with how to go after the cloud capably. Something different, your business chances individual time, rash treatment of data, or information spills.Despite all odds, distributed storage will go down occasionally. Clinical consideration providers need their data to be available whenever, so any get-away on the distributed storage side will adversely influence productivity. This is substantial for business-had physical foundations likewise, anyway associations must rely upon the cloud provider—not themselves—to bring the organization back on the web.All distributed storage based medical services associations must consent to the Health Insurance Portability and Accountability Act (HIPAA). This consolidates healthcare endeavors, yet also loosens up to shows for open minded security, approval of laws, and break cautioning technique. The inhabitants of HIPAA ought to be seen by both the clinical consideration and cloud suppliers in order to ensure HIPAA consistence.Distributed storage networks give security devices that expect to, alert you of, and oversee obscure cloud services. In any case, they are not incredible. The U.S. Part of Health and Human Services’ Office for Civil Rights is presently exploring cases including security breaks of cloud storage based-medical care information. Of those cases, 47% were achieved by hacking or an IT event. Most vendors will have unquestionably a bigger number of limits than the individual customer. Unapproved presentation of information achieves genuine results to the affiliation and significant costs in recovering and restoring data similarly as exhorting impacted individuals.The cloud storage-based healthcare is incredibly defenseless against assaults for a few reasons. To begin with, frequently its segments invest the greater part of the energy unattended; and consequently, it is anything but difficult to assault them truly. Second, the greater part of the interchanges is remote, which makes listening in amazingly basic. At long last, the majority of the cloud storage-based healthcare segments are portrayed by low capacities with respect to both vitality and processing assets and hence, they cannot execute complex plans supporting security. All the more explicitly, the serious issues in cloud storage-based healthcare are identified with security concern confirmation, and information respectability. Verification is troublesome as it for the most part requires fitting validation frameworks and workers that accomplish their objective through the trading of suitable messages between different hubs. Common cryptography calculations spend a great deal of assets with respect to vitality and data transfer capacity, both at the source and the objective. Such arrangements cannot be applied to the cloud storage-based healthcare, given that they will incorporate components, which are genuinely compelled with respect to vitality, interchanges, and calculation abilities. It follows that new arrangements are required ready to give a palatable degree of security ,110.Clinical and medical administrations data not in any way like various kinds of data have serious mystery, assurance, and security concerns. HIPAA consistency is the most significant essential while moving clinical records to the cloud. Moving entire data amassing to a outcast affiliation is certifiably not a basic endeavor to do, especially while moving sensitive information, for instance, clinical consideration data. Altogether more solid security should be ensured in light of the fact that more concerns will develop with access controls, survey controls, affirmation, endorsement, transmission security, and limit security in order to swear off introducing the information to unapproved components. These issues are an obstacle that has moved back cloud determination and should be tended to in order to enable the trustworthiness of cloud structures. Fortunately, immense quantities of the best cloud providers in the market, for instance, Microsoft, Google, and Amazon have obligations to develop the best methodologies and practices to ensure about a customer’s data and assurance ,112.A major search in the cloud-based healthcare is identified with the wide heterogeneity of devices, working frameworks, stages, and administrations accessible and potentially utilized for new or improved applications. Cloud storage platforms heterogeneity is likewise a non-irrelevant concern. Cloud benefits regularly accompany exclusive interfaces, causing asset mix and solution to be appropriately redone dependent on explicit suppliers. This issue can be exacerbated when clients receive multi-cloud draws near, i.e., when administrations rely upon different suppliers to improve application execution and flexibility. These angles are just halfway tackled by cloud expediting, deliberately executed by cloud services or by outsourcing organizations.Interoperability is probably the greatest test while moving medical care frameworks to the cloud. It is because of the huge presence of various conventions, operating systems, programming dialects, stages, information configurations, data sets, and approaches that distinctive medical care associations have been utilizing. Medical services frameworks are not at present planned utilizing normal information demonstrating develops bringing about various information base plans  and in cMedical care frameworks interoperability must happen in a few unique manners: at the supplier, programming, information levels, and framework reconciliation. Suppliers have commonly kept up their own free information and the contrariness of medical care frameworks generally forbids its cross-institutional use . To grasAnother way to deal with creating medical services frameworks ought to be taken so as to plan more interoperable frameworks. This change will bring about various and significant advantages to the healthcare network. Incorporating current medical services frameworks and making them interoperable with the freshest cloud-based advancement seems to be a difficult assignment. Albeit, planning adaptable and versatile guidelines and incorporating clinical information will enormously profit and help the different parental figures.As the quantity of cloud based devices soared, the standardization concern has emerged. The standardization issues are referenced when cloud based devices are applied to a wide scope of orders that are constrained by various administrative gatherings. On account of cloud based storage and distributed computing in medical services, the intricacy turns out to be all the more testing because of the rigid guidelines and clinical norms. Thus, it is vital that cloud based devices producers and diverse administrative gatherings need to set up standard strategies and rules to ensure standardization.Research is as yet required on the execution and advancement of appropriate correspondence conventions, the setting of different cloud-based healthcare standards for advancing interoperability and for scaling the expense of medical services offices, and the evaluation of dangers and vulnerabilities. Additionally, cloud-based medical care acquires similar checking prerequisites from Cloud; however, the related difficulties are additionally influenced by volume, assortment, and speed attributes of medical services.There are two significant challenges that are incompletely related. Legal perspectives are critical and genuine in the momentum research for explicit application situations. The expert co-op needs to adjust to various global laws. Additionally, clients must be furnished with motivators to add to information assortment. Clients could likewise be engaged with new structure squares and devices: quickening agents, systems, and toolboxes that empower the investment of clients in cloud storage based-healthcare as done on the Internet through Wikis and Blogs ,116,117.Medical care organizations coordinate cloud-based storage and distributed computing in existing medical services frameworks by supplanting or including clinical gadgets and sensors into the current gadget organization. Nonetheless, gadgets from various merchants have completely extraordinary correspondence conventions. Thus, it is a test to guarantee a smooth change of these new gadgets. Accordingly, it is obligatory that producers and merchants observe similar norm to ensure that their gadgets uphold in reverse similarity when they are conveyed on a current organization of gadgets.With an expected number of 50 billion gadgets that will be organized by 2021 , expliciThe unpredictable idea of information gathered from wearable gadgets and sensors is another trouble. The intricacy increments when the pace of information created is rising. The executed framework must get ready for the information unpredictability challenge by zeroing in additional on haze registering layers to build the processing force, and utilizing the assets with effective information preprocessing and information examination calculations.Numerous works from scientists, merchants, and governments have been dedicated to making and creating novel cloud storage and healthcare applications. Alongside the momentum research efforts, we empower more bits of knowledge into the issues of promising coordinated innovation, and more directions intending to the open research problems in this paper. In the following, we feature some future research directions in the domain of Cloud storage based-Health applications. An assortment of innovations that are required in Cloud storage based-Health frameworks are as per the following.The gathered datasets by distributed storage registering ought to be put away and documented shaky, simple to utilize and dependable information bases and information stockroom to guarantee that the trustworthiness and security of the information and datasets can be kept at the ideal. As not all the information and datasets can be utilized each time, a smart framework is required in the information distribution center to pick the correct information and datasets for each assistance. Large information is a significant exploration point that is firmly combined with Cloud storage based-Health and with a few related difficulties.Computerized reasoning and AI give the premise to large information handling since enormous information administrations require clever calculations , framewoThe absence of guidelines is considered as a major issue towards Cloud storage based-Health mix by countless scientists. As of now, most things are associated with the Cloud through electronic interfaces, which can lessen the unpredictability of growing such applications. Notwithstanding, they are not explicitly intended for effective machine to machine correspondences and present overhead with respect to arrange burden, postponement, and information preparing. In addition, interoperability is as yet an issue, on the grounds that both the cloud and the things actualize non-standard heterogeneous interfaces.Cloud storage plans have concisely been considered in this paper. For example, we have recently considered them as an operator for the coordination of cloud storage based-Healthcare. Regardless, the literature considers this as a notwithstanding all that open issue as current plans may not offer significant assistance for future directions. One possible bearing to address such issues incorporates the introduction of perceptive storage.Framework and programming structures should be characterized, coded, and executed dependent on the standard, for example, lithe turn of events, Map-Reduce system, and any product structures that can diminish the preparing time and intricacy. Some information investigation despite everything relies upon factual strategies to decipher the implications of relapse and displaying yields. Factual investigation can perform relapse tests and present key yields .Ongoing Cloud storage based-Health applications incorporate progressive data transmission from the things to the cloud, which, accordingly, may rely upon mobile phones as an entry. Such a cycle quickly drains battery limit on both the things and the entry confining the predictable movement to 24 h or less. The writing shows that, in the field carefully identified with the joining of Cloud storage and medical services, acquiring vitality proficiency in both information handling and transmission is a significant open issue.The e-Health Cloud shows an enabling development for some health and medical consideration providers to face various troubles, for instance, rising clinical consideration movement costs, information sharing, and insufficiency of clinical administration specialists. Regardless, the points of interest got are adjusted by issues of trust, assurance, and security aside a couple of particular issues that must be tended to before clinical administrations providers can get trust in the e-Health Cloud. A detailed literature review of cutting-edge cloud storage and medical care components and their related issues was introduced in this paper with an accentuation on the significance of the ideas in concepts, usage, and difficulties. There has been a lot of interesting work done for achieving the higher availability of cloud storage in healthcare systems, which have been compared and critically analyzed in this paper. Among all the currently present state-of-art mechanisms, there is not a single mechanism that could have solved the all availability issue along with the cost, performance, reliability, and storage space efficiency factors. This study also helps to identify the future research challenges for the development of such methods and framework models that could resolve the reliability and availability issues of the current mechanisms in the healthcare environment.The current study will assist the specialists with a helpful base for future work to comprehend the general setting of medical and health services and its applications in distributed cloud storage. In the future, we will likewise attempt to build up a definite report of cutting edge procedures utilized in the investigation of clinical and health care distributed cloud storage information generally in medical services and explicitly in neurology science. This will push the experts to effortlessly uphold dynamic in medical services.


Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, since most farmers are not acquainted with technology. With that in mind, this paper proposes an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment in terms of pH, moisture, texture, colour, air temperature, and light. This platform is coupled with computer vision to further analyze and understand soil characteristics. Moreover, the platform hardware is housed in a specifically designed robust casing to allow easy assembly, transport, and protection from the deployment environment. To achieve requirements of usability and reproducibility, the architecture of the IoT sensing platform is based on low-cost, off-the-shelf hardware and software modularity, following a do-it-yourself approach and supporting further extension. In-lab validations of the platform were carried out to finetune its components, showing the platform’s potential for application in rural areas by introducing digital farming to small-scale farmers, and help them delivering better produce and increasing income. It is evident how agriculture can benefit from the usage of information and communication technology (ICT) with farming-related processes, in the whole value chain, being optimized , resulting in better produce and increased yields, a much desired outcome to cope with the ever-increasing population and food demand. ICT’s relevance in the agriculture domain is reflected in the investments and granted projects on the topic of rural digital transformation  sensing platform that is a collection of software, and low-cost sensors and wireless communication, to make digital farming accessible to small-scale farmers in rural Africa . This plThe proposed IoT sensing platform is part of the Project AFRICA  that aimSensing box based on low-cost, off-the-shelf hardware and software modularity, following a DIY approach for further extension without requirements of extended hardware and software development knowledge.Soil classifier based on computer vision with soil images acquired by a dedicated camera and controlled light system.Standardized 3D-printed protective casing to safeguard all hardware components from the extreme environment conditions, and focused on easy production, assembly and simple integration of the proposed solution.In-lab validation of each component of IoT sensing platform, namely the sensing box, the computer vision, and the casing, targeting further finetuning and improvements to allow a single, integrated platform robust enough for the target application scenarios.The main contributions of this paper can be summarized as follows:This paper is structured as follows—This section provides a general view of ICT enabling digital farming, illustrating its different applications. Then, it focus directly to a list of IoT platforms, implementations and deployments targeting digital farming with potential application in Africa, and that are somewhat related to the IoT sensing platform we propose in this paper.Digital farming is an ever-expanding field focused on the enhancement of agriculture through improved information and communication processes. This involves conceptualizing, designing, developing, evaluating and applying innovative ways to use ICTs in the agriculture domain. ICTs help enhancing agriculture productivity by manifolds. The popularization of wireless technology, sensors, computer vision and image analysis, positioning systems, machine learning (ML), artificial intelligence (AI), among others play a significant role in delivering digital farming to monitor and manage agriculture processes.Within the context of agriculture, the role of digital farming is twofold. First, it shall provide the means for the farmer to measure, as accurately and cost-effective as possible, relevant variables, such as the weather, soil characteristics , external factors . Then, it must produce relevant information to help the farmer take informed decisions regarding his fields, crops, and livestock .Upon this, this section provides a general background on how ICTs have been enabling digital farming in a variety of use cases and application scenarios. It is worth to mention that the goal of this section is not to provide an exhaustive analysis of digital farming solutions, but rather help building the background knowledge towards what is applying ICT in farming. For a comprehensive survey we point the reader to Reference .® (Japanese startup), GAMAYA® ) and Green Sense Farms Holdings® are examples of solutions that rely on gathering sensor data, image and data analysis, and AI to keep track of livestock, to improve farming processes in a more efficient and sustainable way, and to automate farming practices.Farmnote®, FarmBot®, and Mastiline LUCI®. Solutions such as TerrAvion®, Semios®, and Blue River Technology combine image analysis, cloud solutions, sensing, data and predictive analytics, computer vision and AI in order to improve revenue based on precision agriculture, keep track of plant health and manage pest control, and provide a smart machine system that take take decisions at plant level.For solutions considering the usage of the cloud, robots and sensors with the goal of providing a single source of data, controlling robots, and automating processes in dairy farms, one can may come across systems such as MyAgData®, Resson®, and Prospera® provide solutions aiming at improved productivity and that are based on IoT sensors, data and predictive analytics, computer vision, ML, image and data analysis.With special attention to pest control and real-time monitoring of crops, Pycno Agriculture Sensors®, aWhere®, Ec2ce®, and FluroSat® that provide DSS solutions to monitor different farming-related perspective  in order to allow informed decision regarding the farming processes, guide investments, and improve crop performance. Sencrop®, Descartes Labs®, and CowLar Smart® focus either on the state of the fields or of livestock. These solutions bring together sensor and cloud infrastructure, AI, and data analysis to understand the current state of plants, animals, and weather, also providing forecasts and recommendations to allow for taking informed decisions.Regarding Decision Support Systems (DSS) powered with ML, predictive analytics, AI, statistical analysis, and drone and satellite imagery, one may come upon PowWow®, Hortal®, Agrivi®, and Alesca Life® provide solutions looking at different farming processes . The solutions rely on sensors data analysis, and cloud infrastructure to help manage and optimize the target crops.Be it for indoor and outdoor or for soil and hydroponic-based farming, Grownetics®, GreenSeeker handheld crop sensor®, and FieldScout GreenIndex+ Nitrogen App and Board® rely on DSSs, sensors, and smartphone-based applications to gather data on the plants and soil. The data provides insights into the health and needs of the plants and soil, offering the means for taking informed decisions to improve irrigation and fertilization, and to yield better quality produce. Generally speaking, the aforementioned ICT-based solutions look at farming and respective processes from different perspectives as presented next.Type of farming: whether the solutions target rearing of animals or crop cultivation;Purpose of applied technology: focusing on the main goal of the employed solution in the farming processes, that is, monitoring, actuation, control, and automation;Expected outcomes: aiming at better yields, pest and disease control, quality standards, safe transportation, improved storage, nutrient levels, health status, sustainable process.Considered technology: referring to the employed ICT concepts to derive the solutions, that is, sensing, communication networks, computer vision, cloud infrastructure, mobile applications, AI, ML, unmanned vehicles and robots, and so on.Finally, SaturasThis shows how the multifaceted features of ICT enable digital farming. It also serves our purpose of not only having a broader view of digital farming, but also identifying where the proposed IoT sensing platform fits. That is, a solution targeting crop cultivation based on sensor and communication networks, and computer vision to monitor soil conditions to help small-scale farmers achieve better yields. With this background on digital farming at hand, next we provide an overview of different IoT platforms, implementations and deployments targeting e-Agriculture for Africa.General IoT adoption in Africa has been discussed ,9,10 andMoreover, we can identify different works that implement digital farming that are or could be applied in Africa, and that are related to the proposed IoT sensing platform. However, for a more detailed overview of IoT-based solutions and platforms, we again direct the reader to Reference .Combining sensors, long-range communication, cloud infrastructure, and a mobile application, the WAZIUP project  deliversFarmBeats  focuses The IoTecture  is an arAlso focusing on water management, the SWAMP project  brings tAnother way to closely monitor crop conditions and help providing data for informed decisions is to consider Wireless Sensor Networks (WSNs). Haseeb et al.  propose On the same line of combining IoT with WSNs, Garcia et al.  provide Also proposing a low-cost solution, Abba et al.  proposesBy combining low-cost sensors, drones, and data fusion, Spachos  proposesFrom the aforementioned digital farming-based solutions and that have potential application in Africa, one can see that they resort to IoT-based platforms, sensor networks and solutions from different perspectives and a varied set of goals. This shows the multipurpose features of IoT-related solutions when applied in the digital farming domain.However, none of the initiatives have been focused on providing an integrated IoT sensing platform that is easy to reproduce, deploy and use by users, with no to little digital literacy and low income, to seamlessly obtain soil data in rural Africa.As aforementioned, the proposed IoT sensing platform is comprised of—(i) the sensing box (a collection of sensors and the respective software and the host hardware); (ii) the computer vision module , and the protective casing .The sensing box is composed of sensors that provide numeric readings on specific soil and environment parameters  to help triggering the fertiliser production, and to keep farmers/soil experts aware of the soil current status. The computer vision component resorts to camera and illumination control for capturing images and analyzing them with algorithms to provide information on about the soil type where the IoT sensing platform is used. Given the extreme environments on which the platform is expected to be employed  and the fact that is built based on low-cost, sensitive hardware, the platform resorts to its protective casing component to shield the inner parts of the solution.This section starts by presenting the architecture of the IoT sensing platform, followed by a detailed overview of each component.As illustrated in In what concerns the computer vision component, Finally, the design details of the 3D-printed protective casing proposed to house all the hardware , microcontroller, sensors, illumination, mounting parts) that compose the IoT sensing platform are presented in The sensing box component was designed with technology acceptance and reproducibility features in mind. As for the former feature, the sensing box is built based on set of sensors to which soil specialists are used in their daily routine. While the latter feature relates to the fact that the box can be easily reproduced in each target country, considering locally found hardware and avoiding cumbersome importation and customs processes.This section starts by introducing the set of sensors and probes to be considered for building the sensing box, followed by its final hardware and software architectures.The main requirements for considering the following sensors and probes (to be incorporated or connected into the sensing box) were that they be: (i) familiar to soil specialists; (ii) low-cost; (iii) not complex to integrate in the sensing box; and iv) easily found in the local hardware market.LaMotte —offers apH Sensor by Mettler Toledo —offers lIt is worth noting that the information presented in this section comes from the input provided by the National Agriculture Research Organization (NARO) in Uganda, and the Nelson Mandela University (NMU) in South Africa. Both NARO and NMU are partners in Project AFRICA, and are located in the target countries for deployment of the proposed IoT sensing platform. NARO has shared two brands of sensing products normally used by their soil experts:®Jumia, an online retailer present in the Ugandan market that sells sensors for DIY projects, which can be used as a source of low-cost hardware to integrate in the sensing box. As for NMU, the shared information can be divided into three groups:DIY sensors—low-cost sensors with low precision and robustness levels. They can be used in budget-constrained implementations, where the sensor precision and robustness are not mandatory requirements;Commercial grade sensors—sensors sold for commercial applications. More expensive than the DIY sensors, but more reliable and robust;Soil analysis kits—reagent-based products that offer a visual colour matching system to monitor the soil status.In addition to manufacturers shared by NARO, it was possible to identify Considering the information provided, the strategy followed for the development of the sensing box was to evaluate the sensors shared by both NMU and NARO. Thus, we considered the sensors that could be seamlessly integrated in the sensing box and easily used by soil experts, while at the same time being low-cost and available locally. When the sensors failed to comply with the aforementioned requirements, we identified alternatives in the local distributors, comparing them with the ones shared by NARO and NMU and considering those that guarantee compliance and increase the acceptance of the technology by the soil experts.Next, we present the sensing box hardware architecture that resulted from this close analysis on the sensors and probes provided by our Ugandan and South African partners.After understanding what sensors could be used to build the sensing box, we followed with the definition of the hardware architecture to guide its integration development. The rationale that guided such definition was the idea to allow, virtually, any soil expert (or other user with no electronics knowledge) to locally buy the necessary components and connect them in order to build the sensing box on their own. Considering this, we decided to follow a DIY approach for the solution, which ended up being composed by components that can be easily plugged together without the need for complex hardware-related tasks.® as the brain of the sensing box. This is a low-cost SBC commonly used in DIY projects. This device already comes with built-in Wi-Fi support that can be configured in access point (AP) mode, and can easily interface with the camera module. The 8 mega pixel camera module selected (Pi NoIR Camera V2) [The hardware architecture cf.,  of the smera V2)  is capab® that interfaces with all soil and environment sensors. This is done through a header with screw connectors and protoboard space [An Arduinord space , where trd space , while AA 5V DC power supply, which can be composed of standard AC/DC power adaptors compatible with Raspberry Pi and Arduino, or a standard power bank with power specification compatible with Raspberry Pi and Arduino power requirements .DFRobot SEN0249 analogue spear tip pH sensor . This isSentek drill & drop soil moisture, salinity and temperature probe . This isAdafruit 2652 BME280 I2C/SPI temperature sensor . It is wSeeed Studio (TSL2561) 101020030 digital light sensor . This liAll these components can be easily connected to each other using standard equipment and connection interfaces. In addition to the inner hardware of the sensing box, it was also defined the set of sensors to measure pH, moisture, air temperature, light:The selection of this hardware to build the sensing box was performed having in mind a DIY approach, and aligned with the box’s requirements of familiarity, low-cost, simplicity and market availability on the target deployment countries.The same rationale used for the definition of the sensing box hardware was applied for the definition of its software architecture. The objective was to provide a sensing box that is capable of collecting soil and environment parameters, and that can also be easily adapted to new application requirements by users with low to medium level of hardware and software development knowledge. On the hardware side, this was accomplished following a DIY approach, and on the software side we achieve this by providing a set of software modules, and predefined communication interfaces and protocols, that can be easily adapted and extended according the new application scenarios and use cases.The sensing box software architecture is composed of three software modules, with two running in the Raspberry Pi (Camera Reading SW Module and Arduino Readings SW Module) and one running in the Arduino (Multi Sensor SW Module). These SW modules communicate between each other via three sockets, with two for the communications between the Raspberry and the UE connected to it through the provided Wi-Fi AP (ZeroMQ Sensor Data and Configuration sockets), and one for the communication between the Raspberry Pi and the Arduino .ZeroMQ Sockets available in the Raspberry Pi are composed of two types of sockets:Configuration Socket dedicated to communicating configurations for the software modules. In this socket, it is published the configuration for each sensor . The software modules running on Raspberry Pi subscribe to this socket to receive this information and configure themselves according to the configuration received.A Sensor Data Socket dedicated to communicating the data acquired from the sensors. The software modules running on the Raspberry Pi publish in this socket the readings they receive from the sensors.A The communication on these sockets follows a publish-subscribe paradigm, where the software running on the UE opens the Configuration Socket in publisher mode and publishes the configuration message, while the software modules in the Raspberry Pi open it in subscriber mode and consumes the configuration message.The Serial Socket is the socket that establishes the communication between the Raspberry Pi and the Arduino, more precisely between the Arduino Readings SW Module and the Multi Sensor SW Module. In this socket, Sensor Reading Configurations are sent by the Arduino Readings SW Module to the Multi Sensor SW Module, while Sensor Data  are sent by the Multi Sensor SW Module to the Arduino Readings SW Module.The Camera Reading SW Module running on the Raspberry Pi is the module responsible for interfacing with the camera through the CSI interface. This module performs the image acquisition by controlling the camera and illumination, performs the classification of the soil by computer vision and publishes the numerical result of soil class (related with texture and colour) in the Sensor Data Socket, according to the configuration received in the Configuration Socket.The Arduino Readings SW Module running on the Raspberry Pi is responsible to interface with Arduino through the Serial Socket. It configures Arduino according to the configurations received in the Configuration Socket, gets the sensor readings from the Arduino and publishes them in the Data Socket.The Multi Sensor SW Module running on the Arduino is responsible for interfacing with the Sensing Box ambient and soil sensors, through the available physical interfaces . It implements the communication drivers for the sensors and gets the sensor readings according to the configurations received on the Serial Socket. The readings are then provided to the Arduino Readings SW Module through the Serial Socket.The The presented software architecture is ready to be easily integrated in the sensing box hardware and, straightforwardly, communicate with its default sensors, while providing a communication interface for the UE. In addition to this, the software architecture allows for an easy integration of new sensors just by including the sensor driver on the Arduino Multi Sensor SW Module, and its configuration and sensing data in the configuration and sensor data messages. With this, the addition of new sensors in the sensing box is limited to modifications on Arduino software, which is by design thought for low to medium level technical users.Looking at the system as a whole, modifications are also needed in the software that runs on the UE and is responsible for interfacing with the sensing box. The UE needs to send to the sensing box the correct reading configurations  for the new sensor, and correctly interpret the returned readings. As the UE software is outside of the sensing box development scope, it is assumed that such developments are also thought for low to medium level technical users and can be easily adapted to new application scenarios and use cases.The computer vision component of the IoT sensing platform can be divided into the acquisition of soil images and the corresponding soil classification using artificial intelligence.It is worth noting that the component was also thought to be compatible with the referred principles of technology acceptance and high reproducibility of hardware and software. The camera itself is a sensor that works in combination with a set of LEDs actuators that compose the illumination system (including the LED driver). The compatible camera module selected is also available in the African market at a low cost. The software responsible for controlling the hardware parts is running in the SBC and do not require any further configurations in the field.Image texture refers to patterns by contrast variations and an inherent inhomogeneity in natural surfaces resulting from properties such as roughness, depth, illumination and colour.General texture analysis can be used for feature extraction and later for classification. Edge detectors are common image processing methods to identify points in the image which brightness changes sharply and find discontinuities. Edge detectors  can be eThe soil particles distribution is used for soil classification and thus serves as the estimator for water holding capacity and drainage, aeration, susceptibility to erosion and cation exchange capacity, pH buffering capacity. Alternatively, using the library Texture-Color-Geometry Feature Extraction (TCGFE) ,37,38 deOn the other hand, Deep Learning  approachSoil surveys are typically focused on observing and measure soil attributes and properties that were relatively stable or static in time, as the particle size distribution of soil, topsoil thickness or organic carbon matter. The particle size and spatial distribution of soil (soil texture) are properties which influence and control how water infiltrates, how nutrients, chemicals and dissolved substances adhere to surfaces and are retained or transformed, and how energy and matter enter into the soil and is stored or transmitted through it. Soil Texture is the fundamental physical and mechanical property of soils. Soil classification system is essential for the identification of soil properties. Expert systems can be very powerful tools in identifying soils quickly and accurately ,42. ThisWe propose an automated system that has been developed to classify soils in seven classes according to the Food and Agriculture Organization (FAO) World Reference Base soil groups (WRB), and USDA Soil Taxonomy suborders  presenteSoils with strong human influence;Soils with limitations to root growth;Soils distinguished by Fe/Al chemistry;Pronounced accumulation of organic matter in the mineral topsoil;Accumulation of moderately soluble salts or non-saline substances;Soils with clay-enriched subsoil;Soils with little or no profile differentiation.Since we propose an automated system to classify topsoil properties, the images in the referred database are cropped in the topsoil part (about 30 cm in depth) in small images 300 × 300 pixels. The dataset created contains 18,493 images with multiple levels of categorical detail: the first level having 32 Reference Soil Groups (RSGs); the second level with 7 classes simplified according to the WRB; and the third level with 3 classes that are related with the percentage of sand, clay and silt. With this dataset various deep learning architectures are trained to classify in 3 classes  and in 7 WRB classes for reference soil groups , which aThere are other public databases, such as Land Use and Cover Area frame Statistical (LUCAS)  over theThe deep learning architectures used pre-trained in imagenet images, are the VGG16 , InceptiThe models were trained using Adam optimizer , learninnsorflow  backend For testing purposes, we use about 37% of the dataset samples that are not used in the training process. A first global conclusion that we can derive from the comparison of the Regarding the inference throughput, the computer vision component was also validated concerning image acquisition processing time and quality of the captured image. On the Raspberry Pi the loading time for model needs up to 20 second until the model is ready for inference, and the inference time needs about 5 second per image. Thus, in total the time needed to take a picture, preprocess and classify it is about 30 seconds. In this first experimentation, the model used on the Raspberry Pi has the MobileNetV2 deep learning architecture. We expect to optimize this time by converting the final best model with TensorFlow lite to efficiently execute the model on the Raspberry Pi.In order to cope with the outdoor operation and possible deployments in extreme environments , the IoT sensing platform required a robust and protective casing to satisfactorily protect the hardware.To ensure that the casing met the protection needs of the IoT sensing platform, we identified the main requirements that drove the design of the initial concept, namely: (i) prevent moisture and dust from reaching the hardware; (ii) robust outdoor use; (iii) compact; (iv) easy assembly ; (v) based on locally available resources; (vi) easy usability; (vii) safe transport; viii) simple maintenance.The design process started with the definition of an inspiration panel cf., a, focusiWith these requirements in mind and considering the inspiration board, we repurposed the utilization of the vase (hereafter referred to as cabinet), bringing a new meaning to it and aligning with requirement of locally found resources . Within the context of the IoT sensing platform, this repurposed element not only serves to hold the sensing box, but also provides the controlled light zone for image acquisition performed by the computer vision component. This initial concept considers the requirements of protection, simplicity and usability of the case that securely shelters the proposed IoT sensing platform, and that can be easily replicated anywhere in the world. Hence, the concept repurposes the use of a vase, turning it into a cabinet and giving it the function of a lampshade for blocking exterior light (requirement for image acquisition process for the computer vision component).To allow ease production, the protective casing was designed considering the standard 3D printer sizes, thus the maximum dimension of sensing box was set to be 215 mm × 215 mm × 200 mm. These dimensions still allow the sensing box protective casing to be compact and easy to transport. In order to provide a protective casing that allows for an intuitive interaction with the user, all exterior components as well as areas which the user should or not interact/block are duly marked. Buttons, interfaces, sensors are all labelled, and interruption symbols and written information indicate where the user should not interact or place objects on top .The design includes slots to allow proper accommodation for humidity and pH probes, so that they integrate the sensing box when not used or while the box is being transported. The positioning of the slots considered the available internal space in order to guarantee the positioning of all external and internal parts as a single element, providing robustness to the structure as well as good mechanical properties for shock absorption and intense outdoor use.With the external components duly identified, the casing design efforts shifted towards the interior of the sensing box. The main requirement here is that any user can build the sensing box following a DIY approach. For this reason, the interior of casing was designed to allow the hardware parts and cabling to be easily placed and avoid moving, as well as be sealed to avoid entrance of moisture and dust. By carefully considering the purpose of the IoT sensing platform, how it is assembled and transported, the environment it is expected to be used, and the different users that may handle it, the 3D-printed protective casing allows the components to be properly secured, resistance to harsh environmental condition, and robustness to drops (up to 1 meter), thus maximizing the duration of the sensing platform in field deployments.Next, we present the different proofs of concept built for each of the components as well as specific validations carried out on the final prototype to help us having a single, integrated platform robust enough for the target application scenarios.This section is dedicated to the different proofs of concept built towards the integrated IoT sensing platform. It is worth noting that the initial proofs of concept have been built considering what the sensing platform should measure, that is, pH, moisture, texture, colour, air temperature, and light. Besides the Arduino and Raspberry Pi, we have used the Fraunhofer Portugal’s PANDLETS platform  and IoTiFollowing the architecture presented in The evaluation of the soil humidity sensor was performed using an Arduino and the Sentek drill & drop probe with three monitoring depths. As depicted in With this prototype, it was possible to assess the requirements for the SDI-12 communication, namely its sampling requirements and message exchange protocol. Additionally, the prototype allowed us to validate an open source SDI-12 C-based library that can be directly used, or easily adapted, for use in the proposed IoT sensing platform, and its direct support to the Sentek drill & drop probe.To understand the requirements for the pH sensors we tested, as depicted in With this test it was possible to assess the pH sensors requirements both in what regards the hardware interfaces needed to integrate with the analogue output returned by most sensors in the form of a BNC connector, and the firmware needed to interpret the received data. On this proof of concept, the data was being sent to a server inside Fraunhofer Portugal premises where it could be visualized in a web interface cf., b.As aforementioned, we also considered the IoTiP cf.,  within tIn order to be fully compliant to the requirements of the IoT sensing platform, from the hardware standpoint IoTIP would require the addition of a PCB with Wi-Fi modules and different interfaces for the remaining sensors . Software-wise, IoTiP would need the implementation of the Wi-Fi and remaining sensor drivers. Additionally, the compliance with the low level of technical knowledge for further developments (following a DIY approach), and the availability of the components in the local market would not be fulfilled. Despite being focused on easing hardware and firmware development, the technical knowledge required for the development of new features on IoTiP is higher than with Arduino, since the IoTiP is not focused on DIY use cases. For these reasons, the IoTiP ended up not being used.The computer vision proof of concept was developed based on the Raspberry Pi and its camera. Testing a set of image acquisition equipment such as the one presented in Evaluating contrast enhancement and thresholding techniques.Testing feature extraction in different colour spaces - the images accessed in the RGB colour space can be transformed in different colour spaces, and some computations performed, including the statistical, frequency and spatial frequency.These were initially considered using OpenCV, but later dropped due to the machine learning approach adopted.The main validation tasks considered in the proof of concept for the computer vision were:The assembly tests confirm that the camera can be tightly fitted in the slot, but some minor adjustments can be done, mostly tilt and pan, after all the electronics are in place and the images can be previewed. No problems were found on connecting the camera through CSI. The Raspberry Pi 4 must be rotated so that the USB-C power-in port can be exposed.The connectivity between Raspberry Pi and Arduino is done through one of the USB-A ports. Concerning the power supply and control of the LED Driver through the GPIO interface, the Raspberry Pi kernel already comes with a GPIO driver that allows user-mode applications to control the GPIO pins (and LEDs connected to them) directly. However, we decided not to use it but to build a LED driver from scratch avoiding future redesigns, for cases when the current supply from the GPIO driver is not enough for the number of LEDs connected. This way the GPIO only signals the activation of the designed LED driver. The LED driver handles the current needed for the LEDs from the external energy source. All of these aspects can be observed in The first steps towards the final prototype consisted in the assembly of the first group of components into the sensing box cf., , namely After assembling the base of the sensing box, we followed with the assembling the second group of components. Arduino and DC are placed on their supports designed on the structure and then the connectors are glued. The pH PCB connector is placed on its supports and moisture sensor is placed in its specific location. With these two steps, the sensing box is assembled. The box is closed with the cover being screwed into place. This way, we have a robust exterior structure and an isolated interior.The final prototype with the sensing box and cabinet fully assembled are illustrated in With the final prototype, we validated the interaction between the sensing box and the cabinet focusing on a solution that provides an easy connection, is able to create a functional work distance and is built into an unique structure with very good insulation from external factors .Regarding the soil classifier required by computer vision component, we validated considering the following steps: (i) preparation of a Python 3 environment; (ii) installation of tensorflow and keras framework with the specified version for the target device; (iii) management of the installation of all the other dependencies and libraries that keras and tensorflow frameworks rely on; (iv) deployment of the pre-trained and best soil image classification model with the respective weights; (v) adaptation of the main python script to load the best model and perform classification on every image taken.The performance increase available in the Raspberry Pi 4 (comparing it with its predecessors) makes it a very competitive platform for machine learning at the edge. For this reason and due to its small power consumption, we selected this device to implement a pre-trained model to classify soil types in WRB classes. Also, it is possible to use a complete set of linux commands in the preparation of the running environment.The validation of the communication between the Raspberry Pi and Arduino, as well as the sensor integration in the Arduino was performed along with the implementation of the sensing box software modules and communication sockets.As for the validation of communication between the Raspberry Pi and Arduino, configuration messages were used for each sensor, and published in the ZeroMQ socket. The Arduino Readings SW Module correctly interpreted these messages and relayed them to the Serial Socket. The Arduino, on the other hand, was able to interpret the received instructions and communicate with the attached sensors. The collected readings were sent to the Arduino Readings SW Module, that relayed them to the ZeroMQ socket.It is worth noting that both the Adafruit BME280 and Sentek drill & drop soil, provide more information than what is required from the IoT sensing platform, that is, temperature and moisture, respectively. However, this extra information  could be used according to the requirements of the application scenario .These readings could then be consumed by any application subscribing the Sensor Data socket. For the communication between the Arduino and the sensors, we used the Arduino sensor drivers provided by the manufacturers, and the driver implemented in the proof of concept validation.This article proposed and validated an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment coupled with computer vision to classify the type of soil. The platform is based on low-cost, off-the-shelf hardware and software modularity, following a DIY approach and supporting further extension. The hardware is housed in a specifically designed robust 3D-printed casing to allow easy assembly, transport, and protection from the deployment environment.The validation of the sensing box, the computer vision, and the casing was performed in a controlled, in-lab environment, to further fine tune and improve each component prior to testing and deployment in real work environment in Uganda and South Africa.The SW architecture was tested and the socket communication validated, demonstrating that the sensing box is capable of receiving instructions (in the form of configuration messages) from an external device through the ZeroMQ configuration socket, interpret such instructions, collect sensor values via Arduino and communicate the results back to the external device via the ZeroMQ data socket.The computer vision component was also validated concerning image acquisition and processing processes and quality of the captured image. The computer vision component is also capable of delivering processed information to the interface with the ZeroMQ sockets.The 3D-printed protective casing was tested taking into consideration (i) how seamless the process of assembly is; (ii) the fitting and integration of all external and internal components; (iii) the fitting of the sensing box into the cabinet; (iv) the interaction of the user with the platform as whole; and (v) 1-meter drop shock resistance.These in-lab tests carried out with IoT sensing platform have been satisfactory. With a simple setup, we have managed to get readings from the sensors available at data socket. Images were taken from the soil, and the computer vision algorithm is able to return the string value corresponding to the soil texture. These simple, and yet practical, tests demonstrate the viability of the IoT sensing platform to be employed as a support to digital farming.Regarding future steps, which consider validation in the deployments in Uganda and South Africa, we have planned the following—stress tests shall be performed on the sensing box after this integration to understand how the solution behave in extreme scenario such as extended monitoring periods, high sampling frequencies, sensor or software communication errors, and so forth. On top of this, system validation in the field would allow to understand if the system can be mounted by users with low technical knowledge, and whether the DIY approach, in which the sensing box was constructed, requires improvements.