• Kishan T. Karmur
• Ms. Tosal Bhalodia
• Ms. Rupal Shilu

The rapid detection and accurate diagnosis of plant diseases are crucial for ensuring healthy crops and enhancing agricultural productivity. Traditional methods of disease identification, often relying on manual inspection and expert knowledge, are time-consuming and prone to errors. The use of machine learning (ML) techniques to automate plant disease diagnosis and enhance crop health management is investigated in this study. Numerous machine learning models, such as CNN, DT, and SVM, were evaluated for their effectiveness in identifying and classifying diseases from images of plant leaves. By leveraging large datasets of healthy and infected plant images, the models were trained to recognize patterns indicative of specific diseases. The results demonstrate that machine learning models, particularly deep learning approaches, can achieve high accuracy rates in detecting and classifying plant diseases across different crop species. Additionally, the integration of these models with mobile applications and field sensors offers the potential for real-time, on-site disease diagnosis, significantly reducing the time needed for intervention. This study highlights the potential of machine learning in modernizing crop health management, contributing to more sustainable agricultural practices and minimizing the economic losses caused by plant diseases. Future work will focus on enhancing model robustness and expanding the scope to include a wider range of crops.

Machine Learning, Plant Disease Detection, Crop Health, Convolutional Neural Networks, 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 4, page no.n691-n695, April-2025, Available :http://www.jetir.org/papers/JETIR2504D85.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 4, page no. ppn691-n695, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D85.pdf