Machine Learning in Agriculture Tomato Disease Classification Using Random Forest
Keywords:
Machine learning, Random Forest, agriculture, tomato diseases, classification, computer vision.Abstract
This research discusses the use of the Random Forest algorithm for classifying tomato diseases based on visual characteristics of leaves. The relevance of the study is due to the need to automate the process of diagnosing plant diseases in order to increase productivity and reduce costs. The stages of data processing, feature extraction, model training and the mathematical description of the Random Forest method are presented. The results obtained show high accuracy of classification and demonstrate the potential for introducing intelligent systems into the agricultural sector.
References
Lokendra Nath Yogi, Tara Thalal, Sarada Bhandari, "The role of agriculture in Nepal's economic development: Challenges, opportunities, and pathways for modernization," Heliyon, pp. ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2025.e41860, 2025.
Fengzhi Wu, Yong Wang, Maihong Zheng, Jinliang Wang, Jiya Pan, Lanfang Liu, "Prediction and quality zoning of potentially suitable areas for Panax notoginseng cultivation using MaxEnt and random forest algorithms in Yunnan Province," China, Industrial Crops and Products, vol. Volume 229, no. ISSN 0926-6690, https://doi.org/10.1016/j.indcrop.2025.120960, 2025.
Yutong Lai, Ci Peng, Weipeng Hu, Dejun Ning, Luhaibo Zhao, Zhiyong Tang, "Adaptive optimization random forest for pressure prediction in industrial gas-solid fluidized beds,"Powder Technology, vol. Volume 453, no. ISSN 0032-5910, https://doi.org/10.1016/j.powtec.2025.120607, 2025.
Eleftherios Kouloumpris, Konstantinos Moutsianas, Ioannis Vlahavas, "SABER: Stochastic-Aware Bootstrap Ensemble Ranking for portfolio management," Expert Systems with Applications, Vols. Volume 249, Part B, no. ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2024.123637, 2024.
Kaggle.com, "https://www.kaggle.com/," [Online]. Available: https://www.kaggle.com/.
Diego Legarda, Karen Pérez, Daniel M. Muñoz, "A comparative hardware implementation of histogram of oriented gradients as a descriptor in embedded tracking of swarm robots," Journal of Parallel and Distributed Computing, vol. Volume 198, no. ISSN 0743-7315, https://doi.org/10.1016/j.jpdc.2024.105026, 2025.
Yi Zhong, Jie Jiang, Weize Quan, Mingyang Zhao, Dong-ming Yan, "Distinctive learning of latent space feature for occlusion-aware facade parsing," Building and Environment, vol. Volume 279, no. ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2025.112955, 2025.
Yihan Ding, Xuanpei He, Rui Zhang, Haotian Wu, Yingaridi Bu, "Random forest-assisted Raman spectroscopy and rapid detection of sweeteners," Infrared Physics & Technology, vol. Volume 148, no. ISSN 1350-4495, https://doi.org/10.1016/j.infrared.2025.105871, 2025.
Tao Li, Jie-Xue Jia, Jian-Yu Li, Xian-Wei Xin, Jiu-Cheng Xu, "A novel random fast multi-label deep forest classification algorithm," Neurocomputing, vol. Volume 615, no. ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2024.128903, 2025.
Wenxiang Li, Shengqun Chen, Lijin Lin, Li Chen, "Random-forest-based task pricing model and task-accomplished model for crowdsourced emergency information acquisition," Systems and Soft Computing, vol. Volume 7, no. ISSN 2772-9419, https://doi.org/10.1016/j.sasc.2025.200235, 2025.
