A review of plant disease detection and classification methods

Abstract

ABSTRACT The majority of agricultural yields are affected by plant diseases leading to economic losses. Hence, it is necessary to evaluate plant diseases at the very beginning to enhance crop production by making effective control actions. Therefore, automatic plant disease detection models are required to reduce the huge laborious tasks. In recent times, deep learning and computer vision models have been established for the automated detection of plant disease which functions by analyzing the symptoms on plant leaves. Thus, this paper reviews a set of articles that utilizes deep learning and image processing in the field of detecting plant and crop diseases. This survey ensures a comprehensive view of all research works regarding plant disease detection for exploring the effectiveness of the existing models for detecting diseases in diverse crops. This paper aims to analyze various machine learning and deep learning approaches regarding crop disease identification. This review gives an introduction to plant leaf diseases, the conventional methods for detecting plant diseases, the dataset used, the performance metrics utilized for analyzing the existing detection frameworks, and the implementation platforms. Finally, this review provides the limitations in this field of plant leaf detection and the future research trends for further enhancement.