A Review on Machine and Deep Learning Approaches for Crop Disease Detection

A Review on Machine and Deep Learning Approaches for Crop Disease Detection

Sushma B, Syed Rabbith

1Assistant Professor, Computer Science Department, JSS College of Arts Commerce and Science Ooty-Road, Mysuru.

2U.G. Student, Computer Science Department, JSS College of Arts Commerce and Science Ooty-Road, Mysuru

Abstract - Crop diseases significantly affect agricultural productivity and farmer livelihoods. Reducing crop losses and advancing sustainable farming require early detection and response. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have shown promising results in crop disease detection using image analysis. This paper presents a detailed review of ML-based techniques for detecting crop diseases, with a focus on Convolutional Neural Networks (CNNs), transfer learning models, Realtime deployment through web applications like Streamlet, and the shortcomings of the methods used thus far.  We also discuss datasets used in training models, performance metrics, and future trends such as Edge AI, IoT integration, and multilingual support for inclusive smart agriculture.

Key Words:  Machine Learning, Crop Disease Detection, Deep Learning, CNN, Streamlit, Smart Agriculture, Edge AI