Application of Machine Learning Techniques in Crop Disease Classification: A Comprehensive Review

Abstract

Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detection of diseases can help to overcome this problem. Literature suggests, machine learning techniques are capable of achieving these goals in near real-time. This article presents a comprehensive review of machine learning techniques for crop disease detection and classification. Existing plant disease classification systems are explored and commonly used processing steps are investigated. Analysis of machine learning techniques, accuracy factor, and current state-of-the-art in this domain have been reviewed and presented through this manuscript. The survey resulted in the identification of the strengths and limitations of existing techniques and provides a road map for future research works. These would help researchers to understand and pursue machine learning applications in the field of disease detection and classification