Paper Contents
Abstract
In this review paper, we have conducted a thorough investigation of prior and contemporary research on the detection of plant leaf diseases. The conventional manual visual inspection for assessing the quality of plants has proven to be inherently unpredictable and inconsistent. Moreover, it necessitates a substantial level of expertise in the realm of plant disease diagnostics, particularly in phytopathology, leading and genetic to disproportionately lengthy processing times. Consequently, there has been a paradigm shift towards utilizing image processing techniques for the identification of plant diseases. This paper is structured into three principal sections. The first section furnishes a comprehensive review of the various algorithms used, wherein we compare significant algorithms and studies that have employed image processing and artificial intelligence techniques. The second section delves into the frameworks and juxtaposes these against earlier research endeavors. Subsequently, we engage in an in-depth discourse concerning the precision of the outcomes achieved. Drawing insights from our review, we offer a detailed exposition of the performance in detecting and classifying illnesses. Lastly, we consolidate the findings and address the challenges encountered in plant leaf disease detection through image processing. Keywords: Plant Leaf Disease Detection, Image Processing Algorithms, Disease Classification, Precision Assessment, Automated Detection Methods, Feature Extraction, Segmentation Classification.
Copyright
Copyright © 2023 Rakesh Shankar Ghosh. This is an open access article distributed under the Creative Commons Attribution License.
