Comparative Analysis of Machine Learning based Plant Leaf Disease Detection Systems

Recently the availability of Machine Learning(ML) algorithms through state of the art programming interfaces and tools has lead the research community to employ ML based object detection/classification algorithms and techniques for the research work with improved accuracy and performance. There is a significant amount of research work that employs ML based approaches for plant leaf disease detection and classification. A systematic study and comparative analysis of these ML algorithms provide a good insight to researchers and guide them to select and blend these ML algorithms for higher accuracy and performance. In this paper an effort is made to establish comparative analysis of the most recent work done in this domain, The ML algorithms have been analyzed against parameters like the data set, preprocessing techniques, feature extraction methods and accuracy, and the type of diseases that could be detected. Every analysis concisely discusses the characteristics and scope for further extensions of the proposed technique/method.