Comprehensive review of methods for leaf disease identification

Abstract Automatic image classification for plant leaf disease identification (LDI) is an important task in computer vision, food processing, robotics and precision agriculture. Humans classify diseases that occur in plants by careful examination of leaves via visual inspection and knowledge of diseases that can occur to various plants. Similarly, digital images of leaves acquired through devices or from existing datasets are examined via computer vision techniques and classified with trained knowledge using various artificial intelligence approaches. This review discusses several methods explored by various researchers and visualizes their results in terms of the accuracies achieved and also the limitations of each method related to leaf disease identification. The objective of this article is to present a comprehensive review of recent research works by briefly describing the nature, size of data, No of plants and diseases covered, steps involved in the classification approaches, performance and limitation. Thus this article gives overview of recent machine learning (ML), deep learning (DL) and other approaches in LDI. The correctness of LDI classification is presented to the readers by mentioning the accuracy as found from the research articles. Apart from this other efficiency considerations are presented as when needed for describing the research work. This would give a quick summary of review of latest LDI research works by providing black box presentation to the approaches without elaborating the detail steps of the chosen approach.