• Kishan T. Karmur
• Ms. Tosal Bhalodia

Maintaining crop health and raising agricultural productivity depend on the early and accurate detection of plant diseases. Conventional diagnostic techniques, which depend on expert analysis and manual observation, can be laborious and prone to mistakes. In order to provide a quicker and more precise alternative, this study investigates the use of machine learning (ML) approaches to automate plant disease diagnosis. To efficiently detect diseases, a variety of machine learning models were trained using enormous datasets of plant leaf pictures, including Convolutional Neural Networks (CNNs), Decision Trees (DTs), and Support Vector Machines (SVMs). The results show that deep learning techniques, especially CNNs, perform more accurately and efficiently than traditional approaches. Additionally, real-time disease identification is made possible by connecting these models with mobile applications and Internet of Things-based field sensors, which enables prompt intervention and lowers crop losses. The study highlights how machine learning has the potential to revolutionise agricultural disease control and open the door to more environmentally friendly farming methods. The goal of future studies will be to increase the model's adaptability to a variety of crop species and environmental circumstances.

Machine Learning, Plant Disease Detection, Crop Health, Image Classification, Sustainable Agriculture

"Application of Machine Learning for Detecting Plant Diseases and Enhancing Crop Health ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.l591-l596, May-2025, Available :http://www.jetir.org/papers/JETIR2505C57.pdf

"Application of Machine Learning for Detecting Plant Diseases and Enhancing Crop Health ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppl591-l596, May-2025, Available at : http://www.jetir.org/papers/JETIR2505C57.pdf