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

A. Z. Temrokov ; K. Ch. Bzhikhatlov

News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 2, pp. 23-36.

Résumé

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.

Détail

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

Bibliographie

[1] I. A. Borisov, “The relationship between the reproduction of personnel of enterprises and labor resources of territories: the example of Russian agriculture”, Journal of New Economy, 22:3 (2021), 161–183 (In Russian) | DOI

[2] O. Z. Zagazezheva, K. F. Krai, “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), 95–104 (In Russian) | DOI

[3] I. Yag, A. Altan, “Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments”, Biology, 11:12 (2022), 1732 | DOI

[4] S. I. Balaeva, “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), 66–70 (In Russian)

[5] W. W. Turechek, “Apple diseases and their management”, Diseases of fruits and vegetables, v. 1, eds. S. A.M. H. Naqvi, Springer, Dordrecht, 2004 | DOI

[6] N. Ya. Kashirskaya, E. M. Tsukanova, A. M. Kashirskaya, “Protection of apple trees from pests and diseases”, Protection and quarantine of plants, 2010, no. 5, 34–35 (In Russian)

[7] I. M. Khulamkhanov, “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, 1:6 (2013), 133–137 (In Russian)

[8] R. Jashenko, G. Tanabekova, Z. Lu, “Assessment of biological and ecological characteristics of sievers apple tree pests in Trans-Ili Alatau, Kazakhstan”, Sustainability, 15:14 (2023), 11303 | DOI

[9] M. A. Shereuzheva, M. A. Shereuzhev, Z. M. Albekova, “The use of convolutional neural networks for automatic diseases detection tasks”, News of the Kabardino-Balkarian Scientific Center of RAS, 2023, no. 5 (115), 41–51 (In Russian) | DOI

[10] H. Piekarska-Boniecka, “Identification process of selected graphic features apple tree pests by neural models type MLP, RBF and DNN”, Agriculture, 10:6 (2020), 218–227 | DOI

[11] Y. Qian, B. Yang, W. Wang et al., “Apple leaf diseases recognition based on an improved convolutional neural network”, Sensors, 20:12 (2020), 3535 | DOI

[12] H. Yu, X. Cheng, C. Chen, A. A. Heidari et al., “Apple leaf disease recognition method with improved residual network”, Multimedia Tools and Applications, 81:2 (2022), 7759–7782 | DOI

[13] Y. Luo, J. Sun, J. Shen et al., “Apple leaf disease recognition and sub-class categorization based on improved multi-scale Feature Fusion Network”, IEEE Access, 9 (2021), 95517–95527 | DOI

[14] S. Zhang, D. Wang, C. Yu, “Apple leaf disease recognition method based on Siamese dilated Inception network with less training samples”, Computers and Electronics in Agriculture, 213 (2023), 108188 | DOI

[15] B. Liu, K. Fan, W. Su, Y. Peng, “Two-stage convolutional neural networks for diagnosing the severity of alternaria leaf blotch disease of the apple tree”, Remote Sensing, 14:11 (2022), 2519 | DOI

[16] A. I. Kutyrev, “Raspoznavanie i klassifikatsiya boleznei listev yabloni na osnove analiza ikh izobrazhenii modelyami svertochnykh neironnykh setei (CNN)”, Vestnik Ulyanovskoi GSKhA, 2023, no. 3 (63), 215–223 | DOI

[17] Y. Lee, M. P. Patil, J. G. Kim et al., “Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8”, Journal of Agriculture and Food Research, 21:9 (2025), 101840 | DOI

[18] M. A. Kanokova, “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), 126–136 (In Russian) | DOI

[19] A. Z. Temrokov, M. R. Tebuev, K. Ch. Bzhikhatlov, “Concept of a system for forecasting the state of apple orchards”, Collection of scientific papers of the All-Russian scientific and practical conference «Digital transformation of science and education» with international participation, 2024, 102–110, CSTJRH, Nalchik (In Russian)

[20] I. R. Kafiev, P. S. Romanov, I. P. Romanova, “Comparative analysis of robots for apple picking”, Bulletin NGIEI, 2024, no. 10 (161), 33–51 (In Russian) | DOI

[21] K. Bzhikhatlov, I. Pshenokova, “Intelligent Spraying System of Autonomous Mobile Agricultural Robot”, Agriculture Digitalization and Organic Production. Smart Innovation, Systems and Technologies, 362 (2023), 269–278 | DOI