Plant Leaf Diseases Detection Using Image Processing Technique: A Review

Abstract

Plant diseases caused by viruses pose a significant threat to agricultural productivity, affecting a wide range of plant species. With the increasing incidence of these diseases, the demand for rapid and accurate detection methods has grown. Image processing techniques have emerged as a potent tool for spotting disease symptoms in plants in response to this demand. This review paper focuses on the application of image processing techniques to precisely identify viruses in plant leaves, offering automated detection and texture value calculations. The paper presents accurate selection of research papers published over the past five years, chosen based on their titles, abstracts, and conclusions. These papers have been meticulously analyzed and included in this comprehensive review. The paper covers a number of topics related to plant disease detection, such as data sources, feature extraction strategies, pre-processing approaches, data enrichment strategies, and the use of various models for disease detection and classification. Additionally, it explores techniques for enhancing image quality and addressing overfitting issues, ultimately aiming to increase the precision of illness identification. Researchers can learn more about the possibilities of data-driven methods in the field of plant disease identification from this work, which is a great resource. By enhancing system performance and accuracy, the discussed techniques offer innovative solutions for addressing the impact of organisms on plant yields. Researchers and professionals in the agricultural domain will find this review paper a valuable reference in their pursuit of advanced methods for classifying and detecting plant diseases.