Diagnosis And Accurate Classification of Apple Leaf Diseases Using Vision Transformers

In this article the factors that influence the management of agriculture in the context of sustainable development are listed in this article; the main digital technologies have been identified and a model for the use of digital technologies in agricultural management has been built; indicators for evaluating the effectiveness of the use of digital technologies in agriculture and methods for their calculation are proposed.

The efficient functioning of agriculture plays an important role in ensuring food security in many countries of the world. Depending on how insurance protection of agricultural producers is carried out, the competitiveness of their financial stability and competitiveness largely depends. Agricultural insurance is one of the most effective ways to combat agricultural risks and at the same time is one of the most difficult for insurance companies. Insurance events related to natural and climatic phenomena can cause significant losses to insurance companies. The use of reinsurance makes it possible to redistribute insurance liability between a large number of insurance companies, which in turn contributes to increasing their financial stability. The active introduction of digital technologies in agriculture allows not only to increase the efficiency of agricultural production, but also helps to reduce the risk of agricultural business. In turn, reducing the riskiness of agricultural business as a result of market competition among insurance companies will reduce the cost of the insurance policy. The state should pay more attention to supporting the use of digital technologies in agricultural production. Traditional subsidizing does not fully encourage agricultural producers to introduce digital technologies at their enterprises. Insurance plays a significant role in stabilizing agricultural production. To a certain extent , it creates conditions for protecting farmers from the onset of adverse weather events . The use of digital technologies in agricultural business contributes to increasing the efficiency of agricultural production and creates prerequisites for reducing the costs of agricultural enterprises for insurance protection.

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

The use of digital technology in low- and middle-income countries (LMICs) is shaped by multiple factors that can enable or hinder adoption. Understanding these factors is crucial, as LMICs face many constraints that limit the benefits of digital innovation. Therefore, this paper thoroughly evaluates the literature to identify the key determinants influencing the adoption of digital agriculture technologies in LMICs. A total of 30 relevant publications, dated between 2019 and May 2025, were retrieved from the academic databases Google Scholar, Scopus, and Web of Science through a systematic search and analysis approach with clearly defined inclusion criteria to ensure relevance and comparability. This study highlights that socioeconomics, agro-ecological, technological, institutional, situational, social, and behavioral factors are the most frequently discussed influences on digital agriculture technology adoption. Nonetheless, only a few studies have examined all of the components of the complex adoption process, and the majority were only concerned with assessing the impact of a single factor. The findings suggest that these factors positively influence the adoption of digital agriculture technologies, but their effects vary across contexts. The study recommends that policymakers, practitioners, and development agencies adopt integrated strategies that address multiple barriers simultaneously to enhance the uptake and sustained use of digital agriculture technologies.

Discusses the prospects for the use of digital technologies in agriculture. The study of domestic and foreign literature made it possible to identify the author’s approach to the definition of the concept of “digital economy”. Theoretical issues of the development of digitalization in the Republic of Belarus were studied. The main arguments for the development of a green economy in the Republic of Belarus were identified. The issue of development in organizations of the agroindustrial complex of such technology as precision farming was considered. The essence of the dutch Growth Project (system of “illuminated fields”) is disclosed. Possible problems with the use of digitalization in the Republic of Belarus have been identified and ways have been identified to eliminate them with the introduction of such tools as the Mechanism for smoothing the impact of digital transformation on national security and the National (individual) security plan

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms have been proposed to diagnose apple leaf diseases, but most of them are designed to run on high-performance GPUs. This potentially limits their application in the field, in which mobile devices are expected to be used to perform computer vision-based disease diagnosis on the spot. In this paper, we propose a lightweight one-stage network, called the Mobile Ghost Attention YOLO network (MGA-YOLO), which enables real-time diagnosis of apple leaf diseases on mobile devices. We also built a dataset, called the Apple Leaf Disease Object Detection dataset (ALDOD), that contains 8,838 images of healthy and infected apple leaves with complex backgrounds, collected from existing public datasets. In our proposed model, we replaced the ordinary convolution with the Ghost module to significantly reduce the number of parameters and floating point operations (FLOPs) due to cheap operations of the Ghost module. We then constructed the Mobile Inverted Residual Bottleneck Convolution and integrated the Convolutional Block Attention Module (CBAM) into the YOLO network to improve its performance on feature extraction. Finally, an extra prediction head was added to detect extra large objects. We tested our method on the ALDOD testing set. Results showed that our method outperformed other state-of-the-art methods with the highest mAP of 89.3%, the smallest model size of only 10.34 MB and the highest frames per second (FPS) of 84.1 on the GPU server. The proposed model was also tested on a mobile phone, which achieved 12.5 FPS. In addition, by applying image augmentation techniques on the dataset, mAP of our method was further improved to 94.0%. These results suggest that our model can accurately and efficiently detect apple leaf diseases and can be used for real-time detection of apple leaf diseases on mobile devices.

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

Chosen research direction is connected with the problems of achieving a self-sufficient level of economic development of the agrarian sector, improving the quality of life of rural population in Russian regions in the conditions of digital economy. The article highlights the need for the implementation of digital technologies in rural territories of the Russian Federation. It is substantiated that digital transformation is a driver for sustainable agricultural development. A brief analysis of the current state and prospects of rural areas development of the Republic of Bashkortostan in the conditions of a digital economy formation is presented. It is concluded that the majority of agricultural organizations in the region use their productive resources inefficiently and develop disproportionately. The article focuses on the problems of the functioning of health care, education and culture sectors of rural areas of the republic. Main directions of introducing digital technologies in agriculture and in rural areas of the Republic of Bashkortostan are highlighted. It is summarized that as a result of the use of digital technologies, a synergistic effect is formed, which will manifest itself in the development of the economic and social component of the rural area, raising the level and quality of life of rural residents in the region. The research results indicate that the increase in labor productivity in the digital economy will lead to positive changes in the management of human and social capital in rural areas of the Republic of Bashkortostan.

The introduction of precision farming technologies using hightech equipment will increase the productivity of rice, reduce its cost, and improve the environment. The use of digital technologies in agriculture is especially relevant in the face of rising prices for seeds, fertilizers and fuel, as it helps to significantly reduce costs and increase the profitability of agribusiness. The paper reviews the use of unmanned aerial vehicles (UAV) in rice cultivation and describes methods for assessing the state of rice crops. Drones are a more versatile and efficient tool for obtaining data on the state of crops of crops compared to information obtained from satellite images. They allow real-time monitoring of the most important indicators of the state of crops, which allows agricultural producers to make timely decisions. The UAV was used to determine the boundaries of the rice system, terrain, microreliefs of checks, moisture of the surface soil layer and the state of rice crops. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 327 hectares. The main cultivated crop is rice variety Flagman. The survey was performed by a quadcopter with a Mica Sense Red Edge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite data covering the entire analyzed period (06.05.2019 – 08.29.2019). To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

The digital transformation of the agro-industrial complex is a key stage in the modernization of agriculture, which contributes to increasing the efficiency and sustainability of the industry in the face of technological changes. The transformation process can be conditionally divided into several stages. The first stage includes the implementation of digital technologies in the production processes of agricultural enterprises, such as accounting automation, land monitoring, and the use of drones to analyze fields and weather conditions. The second stage involves the optimization of management processes through the integration of complex management systems covering all stages of production. The third stage concerns the development of digital markets, when agricultural enterprises actively use platforms for selling and purchasing products. The final stage is the creation of a single digital environment for the interaction of all participants of the agro-industrial complex. Digitization of the agricultural sector is important for ensuring food security, reducing costs and improving product quality. The introduction of such technologies as mobile applications for agronomists and mechanics, electronic goods and transport invoices and accounting automation software increases the efficiency of agricultural activities. At the same time, legal regulation of the use of digital technologies, in particular electronic signatures, is necessary to ensure the legal significance of electronic documents. An important step in the development of digitalization is the participation of Ukraine in the EU program «Digital Europe», which provides an opportunity to integrate advanced digital technologies and receive funding for innovations. One of the main tools is the State Agrarian Register, which contributes to the transparent distribution of state support among farmers. Despite significant achievements, farmers face problems such as insufficient awareness of the possibilities of digitalization, lack of funding for the implementation of IT solutions, and insufficient training of personnel. Such factors restrain the development of digital technologies in agriculture, which is a threat to the competitiveness of Ukrainian agricultural enterprises in international markets.

Apple leaf lesions present a challenge for their detection and recognition because of their wide variety of species, morphologies, uneven sizes, and complex backgrounds. This paper proposes an improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition. In the proposed YOLOv8, an improved C2f‐RFEM module is constructed in the backbone network to improve the feature extraction of disease object. A new neck network is designed by using C2f‐DCN and C2f‐DCN‐EMA module, which are established with deformable convolutions and efficient multi‐scale attention module with cross‐spatial learning attention mechanism. Moreover, a large‐scale detection head is introduced for increasing the resolution of the small lesion targets, so as to further improve the detection ability for multi‐scale diseases. Finally, the improved YOLOv8 is tested on the common objects in context (COCO) database with 80 kinds of objectives and an apple leaf disease database with 8 kinds of diseases. Compared to the baseline YOLOv8 model, the proposed improved YOLOv8 increases the mAP0.5 by 3%, and decreases the floating‐point operations per second (FLOPs) by 0.3G on the COCO database. For the apple leaf disease database, the improved YOLOv8 outperforms in terms of mAP and FLOPs compared to other models, for parameters and model size, it is ranked second and third, respectively. Experimental results show that the improved YOLOv8 has better adaptability to multi‐scale dense distribution of apple leaf disease spots with complex scenarios.

Background: Plant diseases and pests were natural disasters that seriously threaten agricultural production. Leaf diseases posed a significant threat to the overall productivity and quality of apple orchards. Currently, the diagnosis of plant diseases in apple orchards mainly relied on manual labor, which was both time-consuming and costly. It took a considerable amount of time to train an expert capable of accurately identifying apple diseases and pests, and different crop diseases vary. However, with the use of AI technology, training could be completed in just a few hours to a day, transforming computers into experts in disease and pest identification. Although China became a major apple cultivation nation, the development data from 2021 revealed that traditional advantageous production areas were undergoing dynamic adjustments due to optimization of industrial structure, leading to a decrease in cultivated land. Conversely, the demand for apples in the Chinese market continued to rise, even as the country's population structure gradually trended towards an aging population. This situation underscored the urgent need to replace the laborious and high-cost task of identifying apple foliar diseases in the cultivation process with intelligent agricultural technological solutions. Taking into account the aforementioned factors of the natural environment and socio-cultural considerations and addressing the issue of reduced apple yields due to the high impact of foliar diseases, we initiated study on disease prevention and control during the cultivation process. Methods: To overcome the objective impact of environmental factors, the system extracted images and performed operations such as enhancement and segmentation. Subsequently, a convolutional neural network was employed to process the enhanced images, extracting features such as color, shape, and texture of apple foliar lesions, which were then summarized into histograms using mathematical methods. These histograms served as the primary reference for distinguishing various diseases. Python was chosen as the programming language for the system, and the recognition system was operated by integrating frontend technologies, backend database design, and other relevant techniques. Results: Based on a substantial number of experimental results, the apple leaf disease and nutrient deficiency recognition system could accurately and with extremely high precision identifying major diseases commonly occurring in apple plants, including apple scab, leaf spot disease, rust disease, gray mold disease, and brown spot disease. Moreover, the system was capable of proposing remedies tailored to the specific pain points of each disease. It not only detected apple leaf diseases but also performs nutrient deficiency detection. This holds significant importance in enhancing both the yield and quality of apples. Conclusion: For the commonly occurring foliar diseases in cultivation, the system exhibited the capability to autonomously recognize and provide decision feedback. In contrast to the less efficient and more costly traditional approach of consulting experts, the more accessible apple foliar disease recognition system was poised to replace it as a primary tool for prevention and control assistance. This system will serve as a guiding companion, safeguarding the robust growth of apple plants.

"A perspective on organic agriculture has been presented in the book based on the available information in a cogent and easily understandable manner. In the book, there are 8 chapters oriented towards organic agriculture, environmental, income aspects and modernization in farming practices. Initially, the book provides an overall status of organic farming followed by biofertilizers i.e. granular, liquid and vermicomposting. Different views on various aspects of integrated agricultural management systems, double income of farmers, agricultural technological innovations are furthermore discussed with a focus on technological innovations. Finally, a vision was discussed on transformational role of digital technologies in agriculture. Researcher, teachers, academics and students in the agricultural field in particular will find this book to be of immense use."

Apple leaf diseases (ALD) pose a significant challenge to global apple production, and accurately identifying ALD is crucial for reducing pesticide use and improving fruit quality, particularly in the context of smart agriculture. However, traditional approaches rely on single-model feature extraction, failing to account for relationships between different tasks, which limits their applicability in the apple industry. To address this, we design an optimized convolutional neural network–vision transformer (CNN–ViT) hybrid framework named ResViT-AM, focusing on domain-specific enhancement rather than architectural novelty. Instead of proposing a completely new structure, this work refines existing CNN–Transformer paradigms through task-oriented feature fusion and adaptive attention weighting, tailored for apple leaf disease classification under complex orchard conditions. Using a weighted attention fusion mechanism, our model dynamically integrates features extracted by Residual Network 101 (ResNet-101) and vision transformer (ViT), combining proven architectures in a task-adaptive way rather than pursuing architectural innovation, blending the local convolutional details of Residual Network (ResNet) with the global contextual features of ViT. This approach enhances the model’s representation capability and allows parallel processing of multiple tasks, thereby saving training time and computational resources. Additionally, we evaluate on the public AppleLeaf dataset, which reflects real-world outdoor conditions. On its held-out test split, our model achieves 99.14% top-1 accuracy on the AppleLeaf test split, indicating promising performance under complex orchard conditions. Compared with representative convolutional baselines, ResViT-AM shows greater stability and adaptability on challenging cases, offering a competitive and practical solution for automated apple leaf disease diagnosis.