Not AvailableThe paper reviews various classification techniques exclusively used for plant disease identification. Early stage
plant disease identification is extremely important as that can adversely affect both quality and quantity of crops in
agriculture. For identification of plant diseases, different approaches like image processing, machine learning, artificial
neural networks, and deep learning are in use. This review focusses on an in-depth analysis on recently emerging
deep learning-based methods starting from machine learning techniques. The paper highlights the crop diseases they
focus on, the models employed, sources of data used and overall performance according to the performance metrics
employed by each paper for plant disease identification. Review findings indicate that Deep Learning provides the
highest accuracy, outperforming existing commonly used disease identification techniques and the main factors that
affect the performance of deep learning-based tools. This paper is an attempt to document all such approaches for
increasing performance accuracy and minimizing response time in the identification of plant diseases. The authors
also present the attempts for disease diagnosis in Indian conditions using real dataset.Not Availabl
