• Omprakash Jaiswal
• Dr. J.R. Arun Kumar

Agriculture plays a pivotal role in the global economy and food security. However, crop diseases pose significant threats to agricultural productivity, often leading to substantial economic losses and food scarcity. Early detection and management of crop diseases are crucial to mitigate these effects. In recent years, machine learning (ML) has emerged as a powerful tool for automating crop disease detection, enabling timely and accurate identification of plant health conditions. This review paper provides a comprehensive overview of crop disease detection using machine learning techniques. We explore various datasets, preprocessing methods, machine learning algorithms, deep learning models, performance metrics, real-world applications, and challenges in deploying such systems. Furthermore, we discuss current trends and future directions in the field.

Crop disease detection, Machine learning, K-Means clustering, Multi-Class SVM, Leaf image classification, Plant pathology, Agricultural automation.

"Crop Disease Detection using Machine Learning: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.h528-h536, January-2025, Available :http://www.jetir.org/papers/JETIR2501749.pdf

"Crop Disease Detection using Machine Learning: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. pph528-h536, January-2025, Available at : http://www.jetir.org/papers/JETIR2501749.pdf