Plants are essential for human life as they provide vigor and help mitigate the effects of global warming. Currently, various diseases like leaf spots, blights, rusts, mildews, leaf curling, discolouration, wilting, chlorosis, necrosis, stunted growth and premature leaf drop are affecting plants. These diseases considerably diminish crop yield and quality. Therefore, early revealing of plant diseases is significant in agriculture, as identifying them at a primary stage is important for effective disease management. This article aims to perceive plant leaf diseases using machine learning techniques such as CNN, Random Forest and ResNet-50, which afford faster, more precise and computerized disease finding from leaf images. CNN and ResNet-50 effectively extract composite image features for correct disease classification, while Random Forest provides stable forecasts with reduced over-fitting. These technologies provision early disease diagnosis, reduce pesticide usage and crop losses and support precision agriculture and sustainable farming practices.
Kumar, R., Sonam, K., Shyamrao, I.D., 2026. Plant disease detection using machine learning. Biotica Research Today 8(3), 37-39.
Convolutional Neural Network (CNN), Plant disease, Random forest, Resnet-50
Rana, T.L., Praveen, V., Jayanth, G., Prakash, S.J., 2023. Plant leaf disease detection using machine learning algorithms. B.Tech Thesis, Anil Neerukonda Institute of Technology and Sciences, Sanghivalasa, Bheemunipatnam, Andhra Pradesh. p. 66.
Shelar, N., Shinde, S., Sawant, S., Dhumal, S., Fakir, K., 2022. Plant disease detection using CNN. In: International Conference on Automation, Computing and Communication 2022 (ICACC-2022). ITM Web of Conferences 44, 03049. DOI: https://doi.org/10.1051/itmconf/20224403049.
