Plant Disease Detection Using Image Segmentation Methods

Plant diseases pose a major threat to global farming and can greatly reduce crop production.To reduce their impact, it's important to detect and manage these diseases effectively.One key step in detecting plant diseases is dividing images into smaller parts to clearly identify and analyze the affected areas.This study focuses on the use of image segmentation in plant disease detection, discussing its uses, challenges, and recent improvements which looks at the state of the field.In addition to more sophisticated techniques utilizing machine learning (ML) and deep learning (DL), the study investigates more conventional techniques including thresholding and grouping.The capacity of methods like adaptive segmentation, multiscale analysis, and convolutional neural networks (CNNs) to improve segmentation accuracy across a range of datasets and environmental circumstances is highlighted.The article also covers the incorporation of picture preprocessing techniques, such as contrast enhancement and noise reduction, to increase segmentation results.Even with significant advancements, problems with illumination variability, surroundings difficulty, and plant morphology still exist.The creation of generalizable models is further hampered by the absence of standardized, publicly accessible datasets.These shortcomings are noted in this review, which also emphasizes the necessity of more research to fill in these gaps.This review attempts to direct future research in developing reliable, scalable, and effective image segmentation techniques for plant disease diagnosis by offering a thorough overview of current approaches and their potential.The knowledge acquired advances the more general objectives of global food security and sustainable agriculture.