China has been fully implementing the policy of the cultural and tourism industrial integration since 2018. However, the value-added benefits of this policy are not prominent, and the relationship between industrial integration and the value added to the tourism value chain was seldom addressed by researchers. In the context of China’s high-quality development, it is necessary to conduct the impact of the integration of cultural and tourism industries on the value added to tourism value chain. This paper proposed four theoretical hypotheses and the corresponding econometric models based on the panel data from 2013 to 2020 in China’s Jiangsu Province. According to empirical results, the integration of cultural and tourism industries is spatially unbalanced, with notable imbalances between the south and the north. This paper identified a new connection between cultural and tourism integration and the tourism value chain. It is found that the integration of cultural and tourism industries can enhance the value added to tourism value chain either directly or indirectly through the information technology, with the direct effect being positively moderated by tourism agglomeration. Moreover, this paper may overturn how people generally think about the integration between cultural and tourism industries. It reveals a single-threshold effect that only when the integration of cultural and tourism industries reached a high level will it exert a positive effect. To be more specific, not all Chinese cities are suitable for implementing cultural and tourism integration, because the integration is likely to be ineffective in regions where the cultural industry is substantially less developed than the tourism industry. Cultural tourism refers to tourism activities that involve heritage tourism  and creaNowadays, tourists tend to pursue more authentic experiences from tourism activities, so the boundaries between the cultural and tourism industries are gradually blurred and the scope of the tourism value chain is being expanded continuously . The intTaking Jiangsu Province of China as a case study, this paper aimed to explore the impact of the integration between cultural and tourism industries on the value added to the tourism value chain. Specifically, it addressed the following issues: (a) what is the integration level of cultural and tourism industries. (b) whether this integration level impacts tourism value chain and whether there is any heterogeneity; and (c) how does the industrial integration impact tourism value chain adding. From the perspective of tourism value chain, this paper provided creative responses to the question as how to effectively implement cultural and tourism integration. First, on the basis of the value chain theory, a new connection between cultural and tourism integration and the tourism value chain was identified, which was in line with China’s national strategies of high-quality development. Second, the general impression that cultural and tourism integration can bring over significant benefits was overturned. It was revealed that only high-level integration between the cultural and tourism industries might lead to value addition.The structure of this paper is as follows: Section 2 provides literature reviews on the related topics; Section 3 proposes four theoretical hypotheses; Section 4, Section 5 and Section 6 present the empirical process and results; and Section 7 is the conclusions and implications.The integration between cultural and tourism industries is a common market phenomenon, but not much research has been conducted on this topic yet. The existing literature mainly focuses on two aspects: the evaluation of the current situation and the action mechanism in different aspects.On the one hand, researchers have evaluated the current situation of the regional integration of cultural and tourism industries by measuring the spatial and temporal evolution. Grey relational analysis, data mining , and theOn the other hand, driving factors and influence paths have emerged as hotspots in the research of the related field. The integration between cultural and tourism industries has apparent advantages in promoting industrial and economic development . With thValue chain analysis is widely used in the research of manufacturing industry based on Porter’s value chain theory. However, the uniqueness of the tourism industry makes it necessary to specially define tourism value chain rather than directly adopting the value chain theory of the manufacturing industry. According to Hjalager , there aTourism value chain involves multiple actors and the distribution of value among multiple industries. Through value chain analysis, the dynamic flow of economic and organizational activities among actors of different industries can be easily uncovered by focusing on the inter-linkage. Most of the studies on tourism value chain are qualitative analyses of the governance model , 17, theThe integration of cultural and tourism industries refers to the process of mutual penetration, continuous reorganization, and optimization of culture and tourism elements , which cFirstly, the market of cultural industry has been highly valued by the suppliers due to its excellent quality, high consumption potential, and generally-steady visitor flow . MarketsHypothesis 1a. The integration of cultural and tourism industries can promote the value added to tourism value chain.Tourism has evolved into an important stage featuring the transformation from high-speed to high-quality development . HoweverHypothesis 1b. The value-added effect of high-level integration between cultural and tourism industries is superior to that of low-level integration.Tourism agglomeration refers to the spatial proximity of tourism businesses on the basis of the relationship in the supply chain, which emphasizes the importance of value chain and spatial agglomeration . In viewHypothesis 2. Tourism agglomeration can intensify the promoting effect of integration between cultural and tourism industries on the value added to tourism value chain and play a positive moderating role.Information technology aids industrial integration, which will tear down barriers across industries. The integration of cultural and tourism industries has been substantially improved by information technology, especially during the epidemic when tourist activities are restricted . It can Hypothesis 3. The integration of cultural and tourism industries achieves value added to tourism value chain through information technology. It plays a mediating role in this process.Coupling refers to the motion of a system where the subsystems interact with each other, and coordination means the relationship between subsystems that work together in a harmonious way . The couFirst, the information entropy method is applied to calculate the weights of indicators. It assigns a weight to each indicator mainly on the basis of the information contained in this indicator rather than the data linearity , which cStandardize each positive indicator to eliminate the differences in units.Calculate the proportion of the indicator j in the sample i.Calculate the information entropy of the indicator j.Calculate the weight of the indicator j.Mij and Xij denote the standardized value and the original value of indicator j in the sample i, respectively; maxj(Xij) and minj(Xij) refer to the maximum and minimum value of indicator j among all the samples, respectively; n refers to the number of indicators in each system; m refers to the total sample covering all the cities over the entire observation period.Where Second, the technique for order preference by similarity to an ideal solution (TOPSIS) can reflect the relative importance of each indicator with the time sequence. It serves an effective tool to evaluate the development degree of a subsystem .Calculate the distance from a sample to the positive ideal solution and negative ideal solution.Where Calculate the relative closeness of a sample to the ideal solution.Finally, the improved CCD is used to reflect the integration of the two industries.Calculate the coupling degree following Wang’s  modificaUi is Uc, thenAssuming max Uc and Ut refer to the comprehensive development level of the cultural industry and tourism industry measured by the relative closeness ci, respectively.Where Calculate the coordination degree following Shen’s  modificaα and β are contribution coefficients of the two systems, meeting the condition of α+β = 1.Where Calculate the improved CCD as follow.The evaluation index system was used to calculate the CCD of the cultural and tourism industries in this paper. According to the industrial integration mechanism, the integration of cultural and tourism industries was evaluated in terms of markets integration, supply chains integration and resources integration. Considering the consistency and availability, 14 indicators were selected from 5 aspects: total market revenue, reception volume, core industry income, number of core industries, and number of core resources see . The resLocated in the eastern part of China, Jiangsu is a major economic province nourished by the Yangtze River and the Grand Canal. It is home to several UNESCO world heritage sites, including China’s Grand Canal, Suzhou classical gardens, and Ming Xiaoling Mausoleum, as well as intangible cultural heritages like Kunqu Opera, woodblock and Yunjin embroidery. With a long history, profound culture and picturesque natural sceneries, each city in Jiangsu has its distinctive characters, making Jiangsu a huge tourism market in China. In 2019 (before Covid-19), Jiangsu received 3.9 million overseas tourists and earned $4.74 billion in foreign exchange earnings from tourism. During the May Day holiday in 2023 (the first major public holiday in China after the pandemic), Jiangsu received 39.8 million tourists and brought in tourism revenue of 9.96 billion yuan, ranking the first over the country. Besides, the “Charm of Jiangsu” brand has spread out of China as one of the top three most influential international tourism brands. As one of the most popular tourism destinations, Jiangsu has made outstanding achievements in cultural tourism festivals, performing arts programs and utilization of intangible cultural heritages.The data used in this paper covers 13 cities of Jiangsu Province over a period of 8 years. A fixed effect regression model with a controlling year effect was employed for analysis. Because heteroskedasticity, autocorrelation and cross-section relation might cause biased estimates, the regression with Driscoll-Kraay standard errors was used in the fixed effect model to obtain valid and consistent unbiased estimates as far as possible.First, the main regression model established for the impact of integration between cultural and tourism industries on the value added to tourism value chain is as follows.a0 is a constant, and β1, β2, λk are regression parameters to be estimated; ui refers to the unobserved individual effect; ti refers to the year effect; εi is the random error term.Where i denotes the city and t denotes the year; Second, the panel threshold model is used to analyze the heterogeneity impact of the integration of cultural and tourism industries on tourism value chain. According to the statistical effect, this model may categorize industrial integration into low and high phases by describing the leaping character or structural break in the link between different variables . The panThird, to test the moderating effect of tourism agglomeration on the value added of industrial integration to tourism value chain, an interaction term (CCD*TA) is introduced. The moderation analysis model is established as follows.a1 and b2 are statistically significant, the mediating effect can be established.The mediating effect is an intermediate path underlying the effect of X to Y. Specifically, X influences the mediator variable M (path a is described by Eq ), which Finally, Instrumental Variable Estimates are widely used in solving almost all types of endogenous problems. In this paper, the two-stage least-squares (2SLS) estimates were employed for robust test.The integration level of cultural and tourism industries was taken as the main explanatory variable in this paper, which is calculated through the improved CCD as above. The CCD can reflect the process of benign interaction and synergistic development of the two industries.The value added to tourism value chain was selected as an explained variable. Hjalager  proposedGDPi refers to the GDP of industry i which belongs to one of the six elements of tourism. The Chinese System of National Account divides industries and measures GDP based on the production at the supply side, while the six elements of tourism are defined based on the tourists’ demand. Such a mismatch shows that tourism value chain is actually concealed in different economic departments [Where TVC refers to the value added to tourism value chain. TTR refers to the total tourism revenue. artments . For exaAs a moderated variable, tourism agglomeration reflects the scaling effect of tourism industry and the intensity of tourism activities. Thus, the total tourism revenue divided by regional GDP was used in this paper to measure tourism agglomeration , 42Information technology is a mediating variable. From the industrial level, the postal business volume  has diffThe traffic passenger volume, the upgrading of industrial structure, and the government consumption were selected as control variables. The traffic passenger volume reflects the tourism traffic accessibility , which nJiangsu Statistics Yearbook, Jiangsu Culture and Tourism Statistics Yearbook, China Statistics Yearbook, and the Statistics Yearbooks of the 13 cities. The raw data used for analysis of this paper is as shown in The data used in this paper is the panel data collected from 13 cities of Jiangsu province from 2013 to 2020 (Jiangsu began to conduct statistics on culture-related industries since 2013). As macroscopic statistical data is characterized by authenticity, objectivity and comparability, it is suitable for horizontal and vertical analyses. Thus, all the data used in this paper was directly collected or calculated from Fan and Xue  found thAccording to Under global value chain governance, there are four approaches to increase the industry value: process upgrading, product upgrading, internal chain upgrading, and inter-industry upgrading , 47. TheOf course, the integration between cultural and tourism industries also has negative effects on the tourism value chain. Firstly, the culture and tourism integration advocates resources sharing between hosts and guests, which may exhaust local public resources for the local residents, increase their costs of living and reduce their quality of life . SecondlAs indicated in From et al. [The findings above suggest that the negative effect of the integration of cultural and tourism industries tends to predominate when the CCD of cultural and tourism industries is lower than 0.466. This threshold value is very close to that of Shi et al.  for highet al. . In compet al. . NeverthModel 3  with an et al. [The moderating effect of tourism agglomeration suggests that tourism agglomeration can promote the cultural and tourism industries to the high-level integration. This is consistent with the conclusion of Yan et al. , which aet al.  and agriet al.  have poset al. . Furtheret al. . The touet al. [The causal mediation analysis proposed by Imai et al.  accords Besides, sensitivity analysis indicates how an estimated quantity that violates the key assumption will change for different degrees . A largeet al. [Information technology can make up for the deficiency of the low-level integration of cultural and tourism industries. It is consistent with the view that digital technologies can promote the sustainable development of cultural and tourism industries after crisis . Differeet al.  that infThe coupling degree was used as the instrumental variable because CCD is calculated based on it and the evaluation indicators of coupling degree are different from regression control variables. Model 6 in The policy of cultural and tourism integration, as an important strategy in China’s 14th Five-year Plan, contributes significantly to the tourism high-quality development. The key to high-quality tourism development lies in value addition. Based on these, the paper explored how the integration of cultural and tourism industries affected the value added to tourism value chain. By building an industrial integration evaluation system based on resources integration, markets integration and supply chains integration, this paper evaluated the integration between the cultural and tourism industries of 13 cities in Jiangsu, and applied four econometric models to analyze the impact mechanisms. The following conclusions are drawn from the findings.Firstly, the integration of cultural and tourism industries in Jiangsu Province is generally at an imbalanced stage, with significant differences between the south and the north. Only three cities, namely Nanjing, Suzhou and Wuxi, have achieved high-level integration, all located in economically developed areas. It means that that not all cities are suitable for the policy of cultural and tourism integration, because the integration of cultural and tourism industries is ineffective in regions where the cultural industry is substantially less developed than the tourism industry.Secondly, the integration between cultural and tourism industries not only improves tourism value chain directly but also promotes its value added indirectly through information technology. Information technology plays a mediating role in industrial integration since it can break down barriers across industries. Tourism agglomeration can intensify the promoting effect of cultural and tourism integration on the value added to the tourism value chain, and plays a positive moderating role due to the scaling effect. Thereby, information technology and tourism agglomeration can be used to adjust places that lack integration prerequisites.Thirdly, high-level integration provides a stronger value-added effect than low-level integration. The tourism value chain may be inhibited when the development of the cultural industry lags behind that of the tourism industry by a significant gap. It seems obvious that the two industries cannot achieve high-level integration and maximize the benefits of industrial integration without engaging in synergistic development.Firstly, the tourism value chain involves multi-stakeholder activities  and has Secondly, Porter’s value chain model defines the actions of a firm that create value addition, while tourism is a demand-oriented business that caters to the needs of tourists. The value added to the tourism value chain can occur in four stages . This paThirdly, this paper extended the application of the threshold model to different stages of industrial integration, which are usually analyzed by the average level of CCD. In general, the threshold model is used to address the link between exposure and reaction, such as the relationship between financial constraints and company investment decisions . This moAccording to the impact mechanism between the cultural and tourism integration and the value added to tourism value chain, information technology and tourism agglomeration are two key driving forces for high-level integration. This theoretically supports the government to implement incentives for the integration between cultural and tourism industries. Suppose a region lacks cultural resources but has a sizable tourist base. In that case, it may benefit from the economic value generated by the integration of cultural and tourism industries through the scaling effect and external spillover effect of tourism agglomeration. For regions with abundant cultural resources but an immature tourism market, information technology can be helpful for quickly positioning and responding to the market. It can assist the actors at each node of the tourism value chain in obtaining and sharing market information, so that avoiding the crowding effect of tourism agglomeration.Industrial integration is a stepwise process, and value-added benefits will not be achieved from initial industrial integration but require constant and comprehensive restructuring. It enlightens the government to establish a hierarchical management mechanism for the governance of both cultural and tourism industries. For regions at a low level of integration, the local government should consider implementing exit mechanisms and exploring other forms of tourism development, such as natural science tourism. For regions at a high level of integration, incentive policies can be implemented vigorously to promote the orderly and sustainable development of cultural tourism integration.It should be admitted that this paper has certain limitations in terms of data sampling, model selection and value type. First, the study area is mainly concentrated in Jiangsu province, and the sample is relatively limited. Further studies are required to confirm whether the results of this paper can be generalized. Second, the possible nonlinear relationship between the integration of cultural and tourism industries and the value added to tourism value chain in different stages was ignored. In the future, the negative effect of the low-level integration of cultural and tourism industries and the measures to reduce such effect should be clarified. Lastly, this paper mainly focused on the economic value of tourism value chain, while future research can address the social or other values of tourism value chain.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method. As remote sensing image scenes become more and more complex and the amount of information contained in the images becomes more and more difficult for accurate cross-modal retrieval of remote sensing images, how to extract the required images from a large number of remote sensing images using high-level semantics has become a hot topic in remote sensing image processing applications in recent years. It is important to establish the relationship between non-implicit features and high-level semantics to improve the interpretability of the cross-modal retrieval process and to give users the knowledge they need to better understand the retrieval process and find the desired images.The current stage of research on remote sensing image retrieval is dominated by text-based image retrieval (TBIR)  and contIn order to make up for the shortcomings of the above two image retrieval methods, some scholars began to study the semantic-based remote sensing image re-retrieval method, which obtains the semantic concept of images and uses it as the basis of semantic retrieval . Deep le(1)A bag-of-words association mapping method is proposed by which the semantic retrieval of remote sensing images can be realized. In particular, the visual bag-of-words method is used to solve the problems of too high dimensionality and incomplete description of the underlying features of remote sensing images and to discretize the continuous attribute values in image features by combining the similarity between the features. The FP-growth algorithm used in the method is also improved to quickly obtain the effective rule set.(2)Based on the image semantic retrieval implemented using the proposed method, we propose a feedback mechanism by which incorrect retrieval results can be semantically corrected to reduce the probability of incorrect semantics in them in order to improve the accuracy of the next retrieval.In order to achieve semantic-based remote sensing image retrieval and solve the problem of lack of interpretability in the retrieval process, this paper proposes a bag-of-words association mapping method for establishing the connection between the basic features of remote sensing images and high-level semantics. In this method, the visual feature bag-of-words algorithm is used to discretize the features and reduce the dimensionality. The improved FP-Growth algorithm is then used to obtain the association rules. The proposed method can complete semantic retrieval of remote sensing images with excellent retrieval accuracy and quick retrieval time in the publicly accessible dataset AID with NWPU-RESISC45. Our main contributions are as follows:The rest of the paper is organized as follows: This section reviews the literature relevant to the work in this paper, including recent research on semantic retrieval methods for remote sensing images with interpretability, and image retrieval feedback methods for correcting retrieval results.Implementing semantic retrieval of remote sensing images allows users to retrieve similar images and plays an important role in many practical applications. The key to accomplishing this is to establish associations between the underlying features of the image and the high-level semantics. Association relationships are established using association rule mining (ARM) methods, which can extract potential rules from large amounts of data and provide hidden value services . MethodsTo increase the precision of remote sensing image retrieval, retrieval feedback approaches are crucial . The basThere are many remote sensing image datasets that are used to evaluate retrieval and classification performance, such as the UC Merced dataset (UCMD), the WHU-RS19 dataset, the Aerial image dataset (AID), the RSSCN7 dataset, the NWPU-RESISC45 dataset (NWPU45), and the PatternNet dataset. The UCMD dataset was manually extracted from large images down-loaded from the United States Geological Survey (USGS), selected from aerial orthophotos with a pixel resolution of 30 cm, and consists of 21 land-use/land-cove (LULC) categories with 100 images in each category. Image retrieval studies using locally invariant features on this dataset outperformed standard features such as color and texture . The WHUThe goal of this paper is to achieve semantic retrieval of remotely sensed images by entering search terms. Therefore, we have designed a semantic mapping method for remotely sensed images that converts low-level visual features in the image into high-level semantics. The retrieval method is divided into three steps: (1) The remote sensing image is cut twice, and feature extraction is performed on the small blocks after the cut to obtain a high-dimensional feature matrix. (2) The visual feature word bag is aggregated through the feature matrix of the training set, the image word set is assembled together with its corresponding semantic set to construct the associated text, and the words-to-semantics mapping rules are obtained according to the improved TP-Growth algorithm. (3) The retrieval library, the remote sensing images complete the process from feature extraction to forming semantic expressions, and calculate the similarity between the semantic retrieval words input by the user and the semantics of the remote sensing images. The remote sensing image with higher semantic similarity is used as the retrieval result, and for the wrong image in the retrieval result, a feedback mechanism is used to correct it in order to improve the retrieval accuracy in the next retrieval.Remote sensing image feature extraction is the basis for semantic retrieval. Firstly, the image is sliced twice to improve the efficiency of retrieving small objects in the image. Then, the color and texture features are extracted from the sliced small blocks and the feature matrix of the block is constructed.In the image slicing, the spatial dimension of the remotely sensed image is  and the slicing ratio is To better obtain locally relevant information about the image, we perform twice slices of the remotely sensed image. The first slice cut the original image into After completing the second cut, feature extraction is performed on all blocks in each remotely sensed image. Two potential visual features, texture and color, were selected to characterize the remotely sensed images.Texture feature extractionA local binary pattern (LBP)  was seleThe principle is to use a 3 × 3 window to traverse the remote sensing image to extract features and to compare each feature with the pixel values of its neighbors, using the central pixel as a threshold. If the neighboring pixel value is not less than the threshold, it is marked as 1, otherwise it is marked as 0. In this way, by arranging the values clockwise from the top left corner of the window, a set of 8 binary digits can be obtained, which can subsequently be converted to the decimal system. The decimal digits are used as a unit of texture between 0 and 255. A new image is obtained after the traversal is completed, which is a texture image. As shown in Equation (1),2.Color feature extractionj image pixel values under the i color channel in the remote sensing image, and N is the total number of pixels in the image. In general, the color distribution of an image  can be e3.Feature vector set acquisitionFeature as shown in The extracted features are arranged sequentially, and the texture features and color features are combined into a feature vector Feature extraction is performed on all blocks of each remote sensing image to form the feature vector set Features, as shown in Equation (6),Semantic mapping rules are used to express associations between underlying visual features and high-level semantics of remotely sensed images. A bag-of-words association mapping model is used to obtain this rule.The main process is as follows: (1) Visual feature bag of words construction: Based on the feature vector set of all images in the training set, the K-means clustering algorithm is used to construct a visual feature bag of words with multiple feature vocabularies representing the basic visual features of remote sensing images, completing the mapping of features to vocabularies, which is called first mapping. (2) Association text construction: The set of words of each image is matched with the semantics  of that image, and the words are recorded in the text line by line, forming a words-to-semantics association text. (3) Association rules construction: Based on the association text, the improved FP-Growth algorithm mines rules with strong association between the feature word and the image category to obtain the semantic mapping rules, which is called a two-layer mapping. When two mappings are interconnected by association text, high-level semantic information can be obtained through the underlying features shown in The purpose of this module is to obtain words-to-semantics mining texts by con-structing mappings between features and words.The bag of visual words  feature Visual feature words generationk clustering centers, and each center corresponds to a visual feature word. After that, a dictionary k dimensions can be generated.For all feature vectors, features extracted from a large amount of remote sensing image data , We use the FP-Growth algorithm to mine rule knowledge and obtain association rules for semantic mapping. However, some rules constructed by the traditional FP-Growth algorithm do not contain the meaning of deriving semantics from words. Therefore, the direct use of this algorithm is not conducive to understanding the rules that make up the remotely sensed image and increases the cost of mining.In this study, we improve the FP-Growth algorithm for the issue that the meanings of some rules do not match the semantic derivation of remote sensing images. The basic idea is to divide the frequent item sets obtained by the association rule algorithm into two categories. Frequent item sets containing image categories defined as valid item sets The frequency k is the number of words in Let the current Based on the constructed visual feature bag of words and the words-semantic association rules, we can semantically represent the remote sensing images in the retrieval library. Then, based on the retrieval keywords input by the user, the similarity is calculated with the set of image semantics in the retrieval library, and the images that meet the requirements are obtained in descending order. Finally, feedback is provided for incorrect retrievals to reduce the confidence level of the incorrect semantics in the remote sensing images to improve the accuracy of the next retrieval.The key to accomplish words-to-semantics conversion using semantic mapping rules is that the antecedent rules need to correspond to each other with the words in the remote sensing images. First, the same feature extraction method is applied to the remote sensing images in the dataset to obtain the features. These features are then replaced with words by a visual feature dictionary. TF-IDF) , and the larger the value, the higher the retrieval accuracy.In a remote sensing image to be queried and a retrieved image dataset with the number of images 2.ANMRR)Average normalized modified retrieval rank (Rank(k), indicating their corresponding ranking positions. The semantic retrieval keyword performed each time is SK. The set of images related to the semantic of the retrieved word exists in the remote sensing image retrieval dataset, the number of which is B(SK). In the retrieval list, the first K images are taken. If Rank(k) is greater than K, Rank(k) is set to 1.25 × K, as shown in Equation (21).The images in the remote sensing image retrieval dataset all have SK is denoted by:The average rank The normalized corrected retrieval rank is set to M queries are performed, and we leverage When the performance of the image retrieval method is evaluated in the remote sensing image set, 3.Retrieval timeRetrieval time is taken as one of the evaluation indexes. It includes the processes from the input of retrieval keywords to get the sequence of retrieval results.Any 20 different types of remote sensing images are arbitrarily selected from the dataset of which 70% are used as remote sensing image data for training semantic mapping rules and 30% are used as a remote sensing image retrieval dataset for checking the image retrieval performance. Set both slice ratios to 4 × 4 in the feature extraction module.L denotes the interclass distance, m is the mean of all samples, im is the mean of the samples contained in the clusters, and k is the number of all clusters:D is the intra-class distance, p is a sample, im is the mean of the samples contained in the clusters, and k is the number of clusters to be clustered. The corresponding k value is the best feature word when the value of F is the smallest.To obtain semantic mapping rules required for the semantic retrieval, we need to set some parameters when mining the rules. Since the feature word is obtained from the training data using the K-means clustering center algorithm, an optimal clustering value is used to ensure that different remote sensing image features can be distinguished as much as possible, so the credibility of the information derived from the rules is improved. The method uses a distance cost-effectiveness function with the following equation:A visual feature dictionary is created using the optimal number of characteristics as a base. The Euclidean distance is used to label the remote sensing image features in the training set with the visual feature dictionary to obtain the words-to-semantics association text. The words-to-semantics association texts are substituted into the improved Fp-growth algorithm to obtain the semantic mapping rules. The performance of the improved Fp-growth algorithm is evaluated in terms of the mining time, the number of rules mined, and the efficiency of the rules. For comparison, 500, 600, 700, 800, 900, and 1000 random remote sensing images in the AID dataset and NWPU-RESISC45 dataset are selected for comparison.mAP of different categories in the AID and NWPU-RESISC45.For the performance evaluation of remote sensing image retrieval, the proposed method is compared with the VGG16 and ResNet18 by retrieving Since the VGG16 model and ResNet18 model need to complete semantic retrieval in order to compare with our methods, the adopted strategy is used to complete category annotation for images in the remote sensing image retrieval dataset and this is used to achieve semantic retrieval. The method used in this study is then compared with VGG16, VGG19, ResNet18, ResNet34, and the method in the literature  in termsmAP in the AID dataset were selected for multiple feedbacks, and the changes in the mAP of each category after each feedback were observed.For the performance evaluation of the remote sensing image feedback mechanism proposed in this paper, five categories with low In this section, three main experiments are conducted, all of which are unfolded with a confidence level of 0.6 and a support level of 0.4 settings.Firstly, using the distance cost-effectiveness function, as shown in (25)–(27), the starting number of feature words is set to 2, and by selecting a different number of feature words in ascending sequence. The values of the feature sets derived from the two datasets of AID and NWPU-RESISC45 are calculated. The calculation results are shown in F values in the feature vocabulary obtained for dataset AID and dataset NWPU-RESISC45 tend to stabilize at k = 80 and k = 54. Therefore, according to the distance cost minimization criterion, k = 80 and k = 54 are chosen as the optimal number of feature words for dataset AID and dataset NWPU-RESISC45.As can be seen from In this study, the FP-Growth algorithm is optimized to obtain valuable semantic mapping rules. The reliability of the performance of the optimization algorithm can be seen by comparing the mining time, the number of association rules established, and the percentage of valid association rules with the traditional methods.From the comparison experiments in The impact of the Fp-growth algorithm on retrieval performance before and after optimization is verified in two datasets, and the results are listed in the following table.mAP improved by 16.8% and 10.9%, ANMMR values decreased by 0.1649 and 0.1173, and time decreased by 0.0892 s and 0.671 s. This is because the mined rules containing invalid rules do interfere with the semantic shape calculation performed in remote sensing image retrieval, leading to decrease in retrieval accuracy. A larger number of invalid rules enhance semantic matching time, and thus make retrieval time growth.It can be seen from mAP of each category were calculated in the two datasets for comparison, as shown in The three methods were used to semantically assign the remote sensing images in the AID and NWPU-RESISC45 dataset, respectively, and the retrieval results are obtained using the retrieval keywords. The mAP of 87.5%, improving by 22.5% and 17.6% compared to the other two methods, respectively. However, some of these categories—such as central area, parking lot, and pond—have poor retrieval accuracy. An example of a retrieval of these categories is shown in As shown in mAP of the dataset NWPU-RESISC45 reaches 90.8%, improving 20.3% and 15.6% compared to the other two methods. The retrieval accuracy of image retrieval in the dataset NWPU-RESISC45 is higher than that in the AID dataset. Due to the relatively small image resolution of the NWPU-RESISC45 dataset, which focuses more on the core features described by the image semantic, the features extracted from it can play a better role in semantic differentiation. This leads to a more consistent derivation of each rule with the actual semantic in the process of constructing rules, and thus improving the retrieval accuracy.Similar results are shown in The comparison between the method in this paper and other methods in terms of ANMRR values versus time is listed in As listed in The retrieval examples shown in Five categories with low average inspection rates were selected from the retrieval results of the AID dataset: center, meadow, medium, residential, park, and pond. The results are shown in mAP without feedback.This experiment shows the retrieval results after seven feedbacks, setting the correction value mAP—up to 12%, while the average value reaches 7.02%. The last three feedback results showed an increase, but the increase was relatively insignificant. From the overall trend, the feedback mechanism designed in this paper can effectively correct the semantic probability of error retrieval images and thus improve the image semantic retrieval accuracy. Although the effect of this mechanism on the mAP improvement of each category appears to decrease significantly with the increase in the number of feedbacks, better retrieval results can always be obtained after multiple feedbacks.The first two feedbacks have the largest improvement in the To address the semantic gap between the underlying features and high-level semantic of remote sensing images, the association rule algorithm in data mining technology is used to mine a large amount of remote sensing image data to obtain the semantic mapping rules for deriving high-level semantics of remote sensing images. The semantic mapping rules can automatically assign semantic sets to remote sensing images and complete the semantic retrieval of remote sensing images through semantic sets. Two slices performed on remote sensing images, setting the support and confidence in semantic mapping rule mining, and the semantic probability are important parameters that affect the semantic retrieval of remote sensing images by the method. Our method provides an easy-to-implement idea for semantic-based remote sensing image retrieval.The purpose of setting the slice rate is to extract the small targets from remote sensing images. When the targets representing the dominant semantics of a remote sensing image are extracted as much as possible, then the set of semantics derived for that image is more biased towards its dominant semantics. The first slice determines the number of semantics describing the image, and the second slice determines the number of words describing the semantics. The slice ratio is set for both slices, and experiments are conducted in dataset AID to discuss the effect of the slice ratio on the images retrieved from remote sensing images.As can be seen from The core of this work is to construct semantic mapping rules. The connection between the underlying visual features and high-level semantics of remote sensing images is established by an improved FP-Growth algorithm.Two important pre-set parameters are used in mining the rules: support and confidence. The setting of support affects the composition of frequent item sets and confidence affects the composition of semantic mapping rules. Therefore, different support and confidence levels are set to the impact on the accuracy of remote sensing image retrieval using the AID dataset for mining, so that both slice ratios are 4 × 4, as listed in mAP used to measure retrieval accuracy does not improve due to the increase in rule probability. The possible reason is that the increase in confidence level results in a significant reduction in the derived rules.Analysis of the support in each table gives similar results. Lower or higher support levels lead to a decrease in retrieval accuracy, and the relatively better results are found in the middle interval between 0 and 1. This work selects support and confidence from the middle interval, so good retrieval accuracy is always obtained. However, the optimal support and confidence cannot be pinpointed.It is noted that the retrieval time of the data sets with low retrieval precision is generally slightly higher than that of the data sets with high retrieval precision. This may be due to the obvious difference in semantics, leading to the need for multiple matches during retrieval, thus causing the retrieval time.TF-IDF values together with the rule probability. The more accurate the matching is, the higher the subsequent retrieval accuracy is. Therefore, the effect of adding semantic probability on matching is evaluated by retrieval accuracy in both datasets, as listed in This work realizes the matching of word sets in retrieved images and rule sets in semantic mapping rules by Equation (15) and completes the semantic assignment of remote sensing images according to the matching degree. This work measures the matching degree based on the use of TF-IDF values result in a lower accuracy in remote sensing image retrieval. Only the similarity between the set of rule words and the set of image words is considered to make the result that some rules with low semantic probability match better than those with high semantic probability as matching semantics. This situation generally does not affect the retrieval accuracy. It is contrary to the fact that the higher the semantic probability, the more realistic the resulting rule is. However, a balance between phrase similarity and semantic probability of each rule can be achieved when employing semantic probability along with TF-IDF values to calculate the match, increasing the accuracy of image retrieval. This also implies that the semantic probability is an important parameter for improving the matching degree.(1)The optimized FP-growth algorithm can effectively improve the speed of rule mining by eliminating a large number of invalid rules and making the obtained rule set consist of valid rules as much as possible. The algorithm eliminates the interference by invalid rules in subsequent remote sensing image retrieval, thus improving the accuracy of remote sensing image retrieval.(2)The semantic retrieval of remote sensing images realized by the method used in this study is higher in overall retrieval accuracy than the traditional, and has significantly shortened the retrieval time. This is because the semantic mapping rules obtained with association rule mining on a large amount of image data can construct a strong association between the underlying visual features and high-level semantics.(3)Correction of the semantic probability of errors in incorrectly retrieved images using the feedback mechanism can effectively improve the accuracy of the next retrieval of similar semantic images. In particular, the accuracy will be improved substantially after multiple feedback loops.In this paper, we propose a bag-of-words association mapping method by which the derived semantic mapping rules can visually represent the derivation process from features to semantics as an effective solution to the semantic gap problem in image retrieval. A comparison with different models in two datasets leads to the following conclusions:For remote sensing images with rich spatial information, the semantic information contained in them is not independent and can often be described by multiple semantics together. Therefore, it is necessary to integrate the multidimensional semantics of remote sensing images with the rules when constructing the rules and it is also necessary to retain more semantic information in the extracted remote sensing image features. In addition, support for and confidence in the proposed method are important parameters that affect rule generation. If the parameter is set too low, it will lead to the appearance of rules with low confidence, and conversely, it will lead to the deletion of key rules that may be needed. Then, it is necessary to find an optimal set of support and confidence to get the best retrieval accuracy possible. This will be the main focus of the next research.


The application of mulching film has significantly contributed to improving agricultural output and benefits, but residual film has caused severe impacts on agricultural production and the environment. In order to realize the accurate recycling of agricultural residual film, the detection of residual film is the first problem to be solved. The difference in color and texture between residual film and bare soil is not obvious, and residual film is of various sizes and morphologies. To solve these problems, the paper proposes a method for detecting residual film in agricultural fields that uses the attention mechanism. First, a two-stage pre-training approach with strengthened memory is proposed to enable the model to better understand the residual film features with limited data. Second, a multi-scale feature fusion module with adaptive weights is proposed to enhance the recognition of small targets of residual film by using attention. Finally, an inter-feature cross-attention mechanism that can realize full interaction between shallow and deep feature information to reduce the useless noise extracted from residual film images is designed. The experimental results on a self-made residual film dataset show that the improved model improves precision, recall, and mAP by 5.39%, 2.02%, and 3.95%, respectively, compared with the original model, and it also outperforms other recent detection models. The method provides strong technical support for accurately identifying farmland residual film and has the potential to be applied to mechanical equipment for the recycling of residual film. Mulching film is a plastic film covering the soil surface. As a mature agricultural cultivation technology, mulching film technology has the functions of maintaining soil moisture, improving light conditions, increasing soil fertility, inhibiting weed growth, preventing pests and diseases, and so on ,2. It haIn recent years, image processing techniques have been widely used in agriculture ,12, and With the continuous update of computer technology, deep learning has been gradually applied as a new research direction in machine learning, contributing to artificial intelligence’s realization. Deep learning technology has become an indispensable component for the development of modern agriculture, providing an essential technical force for the realization of agricultural informatization and automation and helping to improve agricultural intelligence. Sun et al.  used UAVIn order to verify the improved model, a small and medium sized dataset for the detection of residual film is made by using residual film images captured in the natural environment;A two-stage training method for the residual film task was designed to improve the problem of less residual film data, effectively improving the model’s accuracy and generalization;An adaptive multi-scale feature fusion module (ASFF) is proposed, which adds learnable adaptive weights to allow the model to adaptively select the appropriate size of feature maps, effectively solving the problem of model missed detection due to the different sizes of residual film;An inter-feature cross-attention (FCA) module is proposed to improve the existing skip-connection mechanism of the model. The correlation feature information of the shallow layer and deep layer is effectively used, and the interference noise in the shallow layer is eliminated.In summary, most of the existing farmland film recognition and detection methods are based on remote sensing images from the use of farmland film mulching as the research object. At this time, the film has the characteristics of a large coverage area and distinctive features . SpectraThe farmland residual film images were collected in the cotton planting area of Yuli County, Bayingoling Mongolian Autonomous Prefecture, Xinjiang Uygur Autonomous Region. The residual film on the surface of the farmland after the operation of a 11JCM-300 residual film recycling machine was used as the research object. A Huawei Mate 40 Pro cell phone was used to acquire the residual film images with a resolution of 4092 × 3072 pixels. During the acquisition, the camera lens was parallel to the ground at a height of about 80 to 100 cm from the ground. Non-conforming images such as blur and jitter were removed, and a total of 2000 residual film images were acquired. The sample contained residual film images with different shooting angles, lighting conditions, and soil moisture content, thus ensuring the diversity of images. The residual film images taken under different environments are shown in The training of the residual film image dataset is inseparable from the location data and the category data of the residual film in the image, which requires labeling of the residual film image. The labeling software Labelme (version 4.5.6) was used to label the residual film images one by one, and each labeled residual film target was given a mulch label, as shown in The dataset of the residual film images is enriched and balanced to better extract the features of the residual film, improve the model’s generalization ability, and reduce the workload of manual annotation. In the training process, online data augmentation was performed on the labeled residual film image dataset, and the dataset was expanded by randomly rotating the image by 5 to 10 degrees, randomly cropping the image, image stitching, image flipping, Gaussian noise, and other data processing methods. Examples of data augmentation are shown in YOLOv5 is a classical single-stage target detection model that requires only one forward propagation to complete the target detection task. It is simpler and more effective than traditional two-stage target detection models such as Faster R-CNN  and SSD An improved scheme was proposed to address the problem of detecting residual film in deep learning. Based on the YOLOv5 classical network, the original spatial pyramid pooling (SPPF) was replaced with an adaptive multi-scale fusion module designed for residual film features, and the model’s recognition of small residual film targets was enhanced by using attention. When combining deep and shallow features, an inter-feature cross-attention mechanism applicable to convolutional networks was proposed to extract high-quality feature information and reduce the useless noise extracted from the residual film images. The training process was also optimized, and a two-stage training strategy was proposed to strengthen the model’s ability to understand the residual film objects with fewer samples. The improved network structure is shown in Due to the environmental conditions, labeling cost, and other factors, we obtained relatively fewer labeled residual film samples. The limited amount of residual film data cannot cover different residual film in real-world scenes, and the lack of diverse residual film leads to the limited contextual understanding ability of the model. When the input image is significantly different from the training data, the model does not have good generalization. At the same time, the limited data will lack some representative residual film data, and the model cannot understand residual film well, which leads to missed detection and wrong detection by the model.Under this condition, in order to make the model converge normally and ensure its generalization, the pre-training weights obtained after a large amount of data training are added to reduce the training difficulty of the model and improve the accuracy. YOLOv5 provides the model weights on the COCO dataset, and using this pre-trained weight to fine-tune the residual film task can help the model converge well. The COCO dataset contains 80 categories in natural scenes, covering almost all common objects in life. However, for this particular residual film target detection task, residual film is often the background class in natural scenes, which is the part ignored by image feature extraction. Therefore, directly using the model weights trained by YOLOv5 on the COCO dataset as the pre-training weights for this task cannot obtain the best results.Therefore, this paper designed a two-stage training strategy for uncommon classes. In the first stage, an improved masked image modeling (MIM) strategy was used to reconstruct only the residual film region in the image, and the original YOLOv5 pre-training weights were replaced by the encoder weights trained in the first stage for the second stage of training. In this way, it could not only reduce the problem of poor model generalization caused by the small number of samples in the dataset, but also solve the problem of difficult model convergence caused by the lack of appropriate pre-trained model weights.The first stage of model training was carried out by referring to the self-supervised training approach. Through image reconstruction, some regions on the image were masked off randomly, and the final goal of the training was to recover the masked-off regions. For the selection of the mask area, the Box parameter in the label was used as the region limit, and only the residual film part of the image was masked. The area of residual film was divided into 64 patches of equal size, from which 50% of the patches were randomly selected for masking. By this reconstruction, the encoder could pay more attention to the residual film information in the image. The first stage and the second stage shared the same encoder. The decoder design of the first stage referred to the U-Net  structurThe feature information in the image can be extracted well using the image encoder. The encoder’s shallow features refer to the input data’s low-level features, such as edges and textures. In contrast, the deep features are the high-level features obtained by multi-layer neural network learning, which can better represent the semantic information of the data. In the structure of YOLOv5, the high-level semantic features obtained from the encoder go through SPPF to obtain multi-scale feature maps. The core structure of the SPPF layer downsampling is MaxPooling, which can quickly achieve multi-scale feature extraction, but only extracts the maximum values, lacking the most core learnability. For the residual film detection task, the simple pooling operation cannot effectively capture the interrelationship between residual film with multi-scale features. Therefore, atrous convolution was used to replace pooling to achieve the same multi-scale feature extraction function by setting different intervals.Smaller residual film objects require smaller receptive fields, and larger residual film objects require larger receptive fields. The consistent single receptive field is difficult to effectively identify objects with significant differences in residual film size. The use of fixed dilated rates in extracting multi-scale features ignores the scale of residual film itself. It also leads to a mismatch between the receptive field of the extracted feature maps and the scale of the residual film object. Therefore, a self-attention scale selection module is designed based on the difference of receptive fields with different scale feature maps. The model can choose the appropriate scale to complete subsequent recognition tasks through self-learning. According to the scale of the residual film object, the feature maps with matching receptive fields will be enhanced, while other feature maps will be suppressed .The specific approach was first to obtain a series of multi-scale features through atrous convolution, and then through the self-attentive selection module. A set of channel dimension weights corresponding to the scales were obtained, and the weights would be larger for the scales suitable for the residual film target. The weights were then combined with the multi-scale features, as shown in Equation . The proYOLOv5 adds a skip connection method in the decoding process, which combines the shallow information extracted by the encoder and the deep information extracted by the decoder to obtain richer semantic information. Observing the residual film image data shows that the residual film objects exhibit strong spatial correlations in space. Although this method of skip connection can make good use of the image feature information extracted by the network, it also introduces a large amount of noise in space for a binary classification task like residual film detection. This is because the shallow features of the encoder extract mainly information such as style and contour. Since residual film is similar in shape to the interference objects on the land surface, it is highly susceptible to noise generation.Therefore, this paper improved the skip connection method to solve the problem of noise introduction. The general skip-connection method directly performs a concat operation on the shallow and deep feature vectors to compose a new feature vector containing rich speech information. If deep and shallow features are simply combined, invalid shallow features may cause the model to detect wrong. The high-level semantic information of the deep layer is utilized to guide the shallow layer information before combining. The residual film feature information in the shallow layer that matches the deep semantics will be selected, which can effectively reduce the effect of useless noise. Referring to the cross-attention mechanism in the Transformer  structurK and V, which denoted the representation information and specific semantic values of the shallow features, respectively. K were used through the dot multiplication of features to obtain the spatial feature attention weight matrix K were used to do the cross product and other operations through the channel feature information after global pooling to obtain the channel feature attention weight V to extract the effective information in the shallow features. With the help of the attention weight matrix, the shallow features were filtered to remove the noise. Finally, the filtered shallow features were combined with the deep features to realize the interaction of shallow and deep feature information.K, and V represent the feature representations obtained by 1 × 1 convolutional mapping of shallow and deep features, The specific process is shown in Equation , for spaEquation , for infThe experiments in this paper were performed on a server with the Ubuntu operating system and CUDA Vision 11.2, and the graphics card was an Nvidia GeForce RTX 3090Ti, with 24 GB video memory. The relevant programming languages and deep learning frameworks were Python 3.7, nvcc 11.0, torch 1.8.0, and torchvision 0.9.0.The experiments were performed on a residual film dataset consisting of photographs of residual film taken in the natural environment and manually labeled. The dataset has a total of 2000 residual film images, divided in a ratio of 4:1 to obtain a training set of 1600 images and a test set of 400 images. The residual film data were enhanced using an online data enhancement strategy.In the training process, the network was trained in two stages. In the first stage, the target detection decoder was frozen, and the image encoder and image generator were trained by randomly masking 60% of the residual film objects. The epoch size for training was set to 100, the batch size was set to 32, the initial learning rate was 0.0001, and the warmup was set to 1000 iterations. In the second stage, the image generator was frozen, and the image encoder and target detection decoder were trained. The training epoch size was set to 30, the batch size was 16, the initial learning rate was 0.001, and the warmup was set to 1000 iterations. The training optimizer for both stages used the AdamW optimizer, and the learning rate tuning strategy was the cosine annealing strategy.In this paper, precision (P), recall (R), mean average precision (mAP), and F1 score were used as evaluation criteria to evaluate the detection performance of the model. The precision indicates the proportion of data detected as positive samples that are true positive samples. The recall indicates the proportion of the total positive samples that the model correctly detects as positive. Higher precision means that a higher percentage of the model’s detection results are residual film targets. The recall indicates the proportion of correctly detected positive samples by the model out of the total positive samples. Higher recall means the model can detect as many residual film targets as possible. The F1 score considers recall and precision equally important, and is the harmonic mean of precision and recall . The preFLOPs and model size are used to measure the model’s computational complexity. FLOPs refer to the number of floating-point operations in the model . Higher FPS and inference time are used to measure the detection speed of the model. FPS refers to the number of images the target network can detect per second. Higher FPS indicates that the model has lower computational complexity and can process data faster. Longer inference time may mean that the model has higher computational complexity and requires a longer inference time to process the input data.In order to better verify the effectiveness of the module, experiments were conducted on combining different modules on a residual film dataset. The experimental parameters were kept consistent during the first stage of training. In the second stage, ablation experiments were performed by adding only the ASFF module, only the FCA module, and both, respectively. At the same time, we also verified the comparison between the two-stage training and the single-stage training loaded with the original pre-training weights, and the results of the experiments are shown in In order to demonstrate the excellent detection effect of the YOLOv5-Atn model, comparative experiments are conducted with the classical target detection networks and the latest YOLO models, including Faster R-CNN, YOLOv3, YOLOv5, YOLOv7, and YOLOv8. The classical and improved models were trained on the manually labeled residual film datasets. To ensure the experiment’s credibility, the models were trained and verified under the same configuration environment, each model was trained three times, and the best result was taken as the comparison indicator. A comparison of the detection effect of different models is shown in The visualization renderings of several residual film images on the original YOLOv5 model, the improved YOLOV5-Atn model, and the latest YOLOv8 model were selected, as shown in Experiments were conducted on the epoch sizes chosen for the pre-training process in the first stage, with ten epochs as intervals, counting the loss of 10 to 150 epochs and the mAP corresponding to the prediction results in the second stage, as shown in The ASFF module uses atrous convolutions of different sizes to extract features from deep features to obtain feature maps of different scales. Although the ASFF module can adaptively give higher weights to the sizes suitable for residual film, the range of size selection is set manually. In order to better verify the effect of different scales on the ASFF module in feature selection, the convolution kernel size is fixed, and ablation experiments are performed on atrous convolutions with different spacing sizes, as shown in The cross-attention module utilized shallow and deep feature interactions to complete the combination of effective information between space and channel, providing richer feature information for subsequent target detection branches at different scales. In the original network structure, there were three places where shallow features interacted with deeper features, denoted as P1, P2, and P3. We verified the effectiveness of the FCA module by taking one, two, and three interaction parts by permutations and combinations, respectively, and the original concat operator was adopted for the connections not using the FCA module. The experimental results are shown in By deep information interacting with shallow information, the FCA module can eliminate the noise information brought by shallow features and better combine image context features. The detection results before and after using the FCA module are visualized using heatmaps, as shown in There may be challenges in applying the proposed method to larger areas or different agricultural landscapes. Extending the method to larger regions or different agricultural landscapes may require acquiring large amounts of data, which can be more difficult and time-consuming. Significant differences exist in the characteristics of different agricultural landscapes, such as crop type and climate. These differences may be reflected in the appearance of residual film, which represents new challenges for the model.The model can be combined with the film pickup device of the residual film recycling machine to be applied to the actual collection process of residual film, including the detection of residual film, spatial location information positioning, and other related parts. It can be deployed and integrated into a complete visual recognition system through a development board. After detecting the residual film information in the image, the coordinates of the center point of the prediction box are converted into the spatial position coordinates of residual film, and the coordinate information is transmitted to the film pickup device to realize the recycling of residual film and increase the recycling efficiency of the residual film recycling machine.Residual film target detection is significant to agricultural production management and environmental protection. This paper proposed the YOLOv5-Atn model for detecting residual film in agricultural fields, which addressed the problem of diverse morphology and size of residual film. A two-stage training strategy was proposed to optimize the training process for solving the problem of poor model generalization brought by limited data samples. The original SPPF structure was replaced with an adaptive multi-scale fusion module of self-design, which allowed the model to adaptively select the appropriate size of feature maps and improved the recognition accuracy of small targets. A cross-attention mechanism between features was proposed for convolutional networks, which effectively removed interference noise from shallow features utilizing the interaction between shallow and deep features. In order to verify the effectiveness of the proposed model in detecting residual film, experiments were conducted on a residual film dataset acquired in the natural environment. Compared with the original YOLOv5 network, the improved model improved by 3.95%, 2.02%, and 5.39% in mAP, recall, and precision, which indicated that the capability of detecting residual film had been effectively improved. The average detection time per image was 0.0128 s, which could meet the demand for real-time detection in agriculture. The detection effect of the improved model was also better than the latest YOLO series of target detection models, and the detection time was faster, which could provide more reliable support for residual film detection.In future work, residual film images from different regions and agricultural landscapes will be collected to expand the dataset. In addition, improving detection performance comes at the cost of increased model complexity and parameters. The subsequent research will focus on the model’s lightweight and improving detection speed to facilitate hardware deployment.


Standard response formats such as rating or visual analogue scales require respondents to condense distributions of latent states or behaviors into a single value. Whereas this is suitable to measure central tendency, it neglects the variance of distributions. As a remedy, variability may be measured using interval-response formats, more specifically the dual-range slider (RS2). Given the lack of an appropriate item response model for the RS2, we develop the Dirichlet dual response model (DDRM), an extension of the beta response model . We evaluate the DDRM’s performance by assessing parameter recovery in a simulation study. Results indicate overall good parameter recovery, although parameters concerning interval width (which reflect variability in behavior or states) perform worse than parameters concerning central tendency. We also test the model empirically by jointly fitting the BRM and the DDRM to single-range slider (RS1) and RS2 responses for two Extraversion scales. While the DDRM has an acceptable fit, it shows some misfit regarding the RS2 interval widths. Nonetheless, the model indicates substantial differences between respondents concerning variability in behavior. High correlations between person parameters of the BRM and DDRM suggest convergent validity between the RS1 and the RS2 interval location. Both the simulation and the empirical study demonstrate that the latent parameter space of the DDRM addresses an important issue of the RS2 response format, namely, the scale-inherent interdependence of interval location and interval width . Personality psychology has a decades-long tradition of using response scales to measure traits Likert, . In stanAs a solution, it might be possible to measure the variability of internal distributions of states or behaviors using an interval-response format. For each question or statement, respondents set a lower and an upper bound to indicate a range of values that best represent their attitudes, behaviors, or experiences. Such an approach can lead to different statistical conclusions compared to using Likert-type scales .Given that we aim at modeling the variability of latent traits, our approach is an alternative to so-called One convenient implementation of an interval-response format is the dual-range slider  shown in Fig. Computerized tests have made it easy to implement continuous response scales for data collection, usually via single-range sliders RS1) as shown in Fig.  as shownBounded responses often have a skewed distribution , which accounts for interval responses on a continuous bounded scale. For this purpose, we evaluate parameter recovery in a simulation study. Moreover, we assess the model’s fit to data in an empirical example for an Extraversion questionnaire based on posterior predictive checks and leave-one-out cross-validation.The second aim concerns the validation of the person parameters of the proposed IRT model. We assume that the locations of the response intervals of the RS2 correspond to the central tendency of a latent trait. To test this assumption, we assess the convergent validity of the model’s location parameters by comparing the corresponding estimates to those obtained by fitting the BRM to RS1 responses. We expect a high correlation  differ when relying either on manifest mean scores or on latent parameter estimates. First, we again consider the correlation of the location estimates of the RS1 and the RS2 format, expecting higher convergent validity for the model parameters than for mean scores. Second, we assess whether the scale-inherent correlation among the two dimensions expansion and location is smaller for the model-based than the descriptive estimates. For this purpose, concerning the manifest mean scores, we focus on the correlation of the interval width and the absolute deviance of the response-interval location from the scale midpoint. Concerning the model parameters, this corresponds to the correlation of the person expansion parameter and the absolute value of the person location parameter. Higher convergent validity and a smaller internal correlation among the two dimensions would justify the employment of the proposed model.In the following, we outline the BRM   s . We simulated 300 datasets for To investigate the parameter recovery of the DDRM, we conducted a simulation study. All R scripts are available at the Open Science Framework  as an alternative representation of a given response interval,The primary goal of our empirical study was the collection of a suitable data set for the development and evaluation of the DDRM. The secondary goal was to compare the DDRM location parameters to those of the BRM. In an effort to maximize the number of items and respondents, we decided to split neither the sample nor the item pool. Instead, for the standard single-range-slider format (RS1), we used a different set of items from an established measurement instrument ,To address research questions regarding the correlation of person parameters across different response formats, it is convenient to combine the BRM and the DDRM into a joint model. For this purpose, we assumed a multivariate normal prior distribution for the person parameters of both models . At the level of respondents, for RS1 responses, the mean percentage of responses The fit of Bayesian models can be evaluated via graphical checks . The basic principle of LOO is to fit a model on a dataset multiple times while holding out one response at a time  and the DDRM concerns two-dimensional data (location and expansion), a direct comparison of the corresponding item-precision parameters is not meaningful. Nonetheless, within each model, low precision can be used to detect potentially problematic items. In case of the DDRM, this means that respondents answered the respective item in a way that was not consistent with responses given for other items, both regarding interval location and interval width. To give an intuition, we discuss the two items with the lowest precision parameters. The content of these items reveals the potential pitfalls of using an interval-response format. For instance, the item “I am not easily amused” suggests that the use of items that involve more than one semantic direction to reason about  may be especially problematic when using the RS2 format. Moreover, the item “I love surprise parties” could pose the problem that surprise parties do not happen very frequently, and consequently, respondents might not have had a sufficient number of experiences to assess the variability of their agreement. It is also illustrative to consider the three items with the highest precision parameters: “I cheer people up”, “I feel comfortable around people”, and “I make friends easily.” We can expect respondents to have experienced multiple instances of situations where the described behaviors could have potentially occurred. Overall, this means that the precision parameter is useful for evaluating the alignment of the location and expansion dimension of an item. Precision can only be high if an item allows for a good discrimination in both dimensions. On the flip side, low precision estimates can be used to detect (and possibly remove) inconsistent items.Lastly, the three scaling parameters were very similar in size across models and dimensions  are necessarily correlated due to the bounded response scale. In contrast, the DDRM is able to recover the correlation structure of the latent location and expansion parameters, even if the true correlation is zero. To investigate these issues empirically, we computed the correlations of interest for raw mean scores and for the latent DDRM parameters. In the case of manifest responses, again, we computed the correlation between the mean scores for absolute deviance from the scale midpoint  response format in terms of parameter recovery and model fit. The simulation study demonstrated a good recovery of the DDRM’s parameters. However, the precision of the estimated person expansion parameters Regarding model fit in our empirical example application, the results for the DDRM were ambiguous. Model-performance statistics (LOO) were unproblematic while the graphical model checks revealed some misfit. The posterior-predicted distributions for the lower and upper bound of the response interval showed a satisfactory fit, but the DDRM predicted too many narrow intervals in the middle of the response scale. Thus, the model seemed to be lacking flexibility regarding the response-interval widths. However, to our knowledge, there is no competitor model against which our model could have been tested. By developing the DDRM, we proposed a first IRT modeling approach for interval responses, which can be further refined for future applications.As a second aim, we focused on the convergent validity of the two response formats single-range slider (RS1) and RS2 and the corresponding models. For this purpose, we assessed the correlation of person location parameters estimated by the BRM and the DDRM. This correlation was very high, which provides evidence for the convergent validity of the BRM and DDRM location parameters, and consequently, also of the RS1 and RS2 formats. Hence, the RS2 format may be used in place of the RS1, especially if not only the location dimension but also the expansion dimension is of interest. Thereby, our study contributes to the literature by providing partial evidence for the validity of the interval-response format through direct comparison to a well-established response format . Concerning scale-inherent dependencies, the two person parameters of the DDRM for the location and the expansion dimension were less correlated than the corresponding raw mean scores . This provides evidence for the discriminant validity of the DDRM person estimates on the two dimensions. Thus, we provide a model-based alternative to correction methods that aim at compensating for the detrimental effects of the bounds of a response scale . Hence, future studies should avoid showing exact numerical values or anchors for round figures. Furthermore, we did not control for the type of digital device used by the respondents, which might have influenced response behavior.We developed a new IRT model for interval responses, the Dirichlet dual response model (DDRM), as an extension to the beta response model (BRM; Noel & Dauvier,