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Intelligent recommendation system for apple orchard protection in the Kabardino-Balkarian Republic

A.Z. Temrokov, K.Ch. Bzhikhatlov

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Abstract: One of the important areas of agriculture is fruit gardening, in particular, intensive apple orchards make a significant contribution to the agricultural sector of the Kabardino-Balkarian Republic. At the same time, to preserve the harvest, it is necessary to ensure timely detection and elimination of threats associated with apple diseases and pests. Given the shortage of specialized specialists, the task of developing an automated system for recognizing diseases and pests of apple orchards becomes urgent. For this purpose, the study set the goal of developing and assessing the applicability of an intelligent recommendation system for the protection of apple orchards in the KBR. This article describes the concept and presents the results of the development of a system for monitoring the condition of apple orchards, designed to identify diseases and pests on trees, as well as select the most appropriate plant protection plan depending on the location of the orchard. The program is a web application created on the basis of the FastAPI, Vue.js frameworks and a neural network, responsible for recognizing pests and diseases of apple trees from a photograph and drawing up an optimal plan for their treatment. The results of training a neural network on a prepared sample of photographs of healthy and infected apples are presented. Various models were used as a basis for the neural network: Roboflow 3.0, RF-DETR, YOLO v11 and YOLO v12. The developed service will allow diagnosing apple tree diseases with minimal time delays, as well as ensuring the selection of protection methods, if necessary, which will reduce the risks of crop loss by gardeners. As a result of testing the model, the Roboflow 3.0 model achieved the best indicators: mAP was 91.0%, precision 97.5%, and recall 88.5%, which indicates the applicability of the approach. In order to expand the list of recognizable threats and improve accuracy, it is planned to collect additional photographic materials in the republic’s orchards, including photographs of leaves and trunks of apple trees, and further testing with the participation of gardeners of the republic.

Keywords: image recognition, apple tree, apple diseases, recommendation system, machine learning, internet service

For citation. Temrokov A.Z., Bzhikhatlov K.Ch.Intelligent recommendation system for apple orchard protection in the Kabardino-Balkarian Republic. News of the Kabardino-Balkarian Scientific Center of RAS. 2025. Vol. 27. No. 2. Pp. 23–36. DOI: 10.35330/1991-6639-2025-27-2-23-36

References
  1. Borisov I.A. The relationship between the reproduction of personnel of enterprises and labor resources of territories: the example of Russian agriculture. Journal of New Economy. 2021. Vol. 22. No. 3. Pp. 161–183. DOI: 10.29141/2658-5081-2021-22-3-9. EDN: FYAPLA. (In Russian)
  2. Zagazezheva O.Z., Krai K.F. Problems and prospects for the introduction of robotic and intelligent technologies in plant growing. News of the Kabardino-Balkarian Scientific Center of RAS. 2021. No. 6(104). Pp. 95–104. DOI: 10.35330/1991-6639-2021-6-104-95-104. EDN: WXLZCP. (In Russian)
  3. Yağ I., Altan A. Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology. 2022. Vol. 11. No. 12. P. 1732. DOI: 10.3390/biology11121732
  4. Balaeva S.I. Status and prospects for the development of intensive gardening in the Kabardino-Balkarian Republic. News of the Kabardino-Balkarian State Agrarian University named after V.M. Kokov. 2018. No. 1(19). Pp. 66–70. EDN: YWALOH. (In Russian)
  5. Turechek W.W. Apple diseases and their management. In book: Diseases of fruits and vegetables. Vol. 1. Eds: Naqvi S.A.M.H. Dordrecht: Springer. 2004. DOI: 10.1007/1-4020-2606-4_1
  6. Kashirskaya N.Ya., Tsukanova E.M., Kashirskaya A.M. Protection of apple trees from pests and diseases. Zashchita i karantin rasteniy [Protection and quarantine of plants]. 2010. No. 5. Pp. 34–35. EDN: MBCNMJ. (In Russian)
  7. Khulamkhanov I.M. The influence of climatic factors on the mass flight of the codling moth (Laspeyresia pomonella L.) in the Kabardino-Balkarian Republic. News of the Kabardino-Balkarian Scientific Center of RAS. 2013. No. 6-1 (56). Pp. 133–137. EDN: RPXLWV. (In Russian)
  8. Jashenko R., Tanabekova G., Lu Z. Assessment of Biological and Ecological Characteristics of Sievers Apple Tree Pests in Trans-Ili Alatau, Kazakhstan. Sustainability. 2023. Vol. 15. No. 14. P. 11303. DOI: 10.3390/su151411303
  9. Shereuzheva M.A., Shereuzhev M.A., Albekova Z.M. The use of convolutional neural networks for automatic diseases detection tasks. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 5(115). Pp. 41–51. DOI: 10.35330/1991-6639-2023-5-115-41-51. EDN: GOUNQN. (In Russian)
  10. Boniecki P., Zaborowicz M., Pilarska A.A., Piekarska-Boniecka H. Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN. Agriculture. 2020. Vol. 10. No. 6. Pp. 218–227. DOI: https://doi.org/10.3390/agriculture10060218
  11. Qian Y., Yang B., Wang W. et al. Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network. Sensors. 2020. Vol. 20. No. 12. P. 3535. DOI: 10.3390/s20123535
  12. Yu H., Cheng X., Chen C., Heidari A.A. et al. Apple leaf disease recognition method with improved  residual  network.  Multimedia  Tools  and  Applications.  2022.  Vol.  81.  No.  2. Pp. 7759–7782. DOI: 10.1007/s11042-022-11915-2
  13. Luo Y., Sun J., Shen J. et al. Apple Leaf Disease Recognition and Sub-Class Categorization Based  on  Improved  Multi-Scale  Feature  Fusion  Network.  IEEE  Access.  2021.  Vol.  9. Pp. 95517–95527. DOI: 10.1109/ACCESS.2021.3094802
  14. Zhang S., Wang D., Yu C. Apple leaf disease recognition method based on Siamese dilated Inception network with less training samples. Computers and Electronics in Agriculture. 2023. Vol. 213. P. 108188. DOI: 10.1016/j.compag.2023.108188
  15. Liu B., Fan K., Su W., Peng Y. Two-Stage Convolutional Neural Networks for Diagnosing the Severity of Alternaria Leaf Blotch Disease of the Apple Tree. Remote Sensing. 2022. Vol. 14. No. 11. P. 2519. DOI: 10.3390/rs14112519
  16. Kutyrev A.I. Recognition and classification of apple leaf diseases based on the analysis of their images by convolutional neural network (CNN) models. Vestnik Ul’yanovskoy GSKHA [Bulletin  of  the  Ulyanovsk  State  Agricultural  Academy].  2023.  No. 3(63).  Pp. 215–223. DOI: 10.18286/1816-4501-2023-3-215-223. (In Russian)
  17. Lee Y., Patil M. P., Kim J. G. et al. Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8. Journal of Agriculture and Food Research. 2025. Vol. 21. No. 9. P. 101840. DOI: 10.1016/j.jafr.2025.101840
  18. Kanokova  M.A.  Analysis  of  the  effectiveness  of  the  use  of  software  and  robotic systems for the protection of agricultural crops from diseases, pests and weeds. News of the Kabardino-Balkarian Scientific Center of RAS. 2021. No. 6(104). Pp. 126–136. DOI: 10.35330/1991-6639-2021-6-104-126-136. (In Russian)
  19. Temrokov A.Z., Tebuev M.R., Bzhikhatlov K.Ch. Concept of a system for forecasting the state of apple orchards. Sbornik nauchnykh trudov Vserossiyskoy nauchno-prakticheskoy konferentsii «Tsifrovaya transformatsiya nauki i obrazovaniya» s mezhdunarodnym uchastiyem. [Collection of scientific papers of the All-Russian scientific and practical conference “Digital transformation  of  science  and  education”  with  international  participation].  Nalchik,  2024. Pp. 102–110. EDN: CSTJRH. (In Russian)
  20. Kafiev I.R., Romanov P.S., Romanova I.P. Comparative analysis of robots for apple picking. Vestnik NGIEI [Bulletin NGIEI]. 2024. № 10(161). Pp. 33–51. DOI: 10.24412/2227-9407-2024-10-33-51. EDN: YHIYRS. (In Russian)
  21. Bzhikhatlov K., Pshenokova I. Intelligent Spraying System of Autonomous Mobile Agricultural Robot. Agriculture Digitalization and Organic Production. Smart Innovation, Systems and Technologies. 2023. Vol. 362. Pp. 269–278. DOI: 10.1007/978-981-99-4165-0_25
Information about the authors

Aidemir Z. Temrokov, 2nd year Student in the field of Informatics and Computer Science, Kabardino-Balkarian State University named after Kh.M. Berbekov;

360004, Russia, Nalchik, 173 Chernyshevsky street;

temrokovaydemir@gmail.com, ORCID: https://orcid.org/0009-0005-2272-475X, SPIN-code: 4961-2069

Kantemir Ch. Bzhikhatlov, Candidate of  Physics and Mathematics sciences, Head of the Laboratory of  Neurocognitive Autonomous Intelligent Systems, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;

360010, Russia, Nalchik, 2 Balkarov street;

haosit13@mail.ru, ORCID: https://orcid.org/0000-0003-0924-0193, SPIN-code: 9551-5494

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