Detection of Late Blight Disease in Tomato Leaf Using Image Processing Techniques

TL;DR

This paper presents an image processing approach using segmentation and Multi-class SVM to detect late blight disease in tomato leaves, aiming for early diagnosis to reduce crop loss.

Contribution

It introduces a novel taxonomy and model for late blight detection, analyzing current trends, challenges, and future research directions in image-based disease diagnosis.

Findings

Effective segmentation of damaged leaf areas

Reliable disease classification with Multi-class SVM

Identification of research gaps and future prospects

Abstract

=One of the most frequently farmed crops is the tomato crop. Late blight is the most prevalent tomato disease in the world, and often causes a significant reduction in the production of tomato crops. The importance of tomatoes as an agricultural product necessitates early detection of late blight. It is produced by the fungus Phytophthora. The earliest signs of late blight on tomatoes are unevenly formed, water-soaked lesions on the leaves located on the plant canopy's younger leave White cottony growth may appear in humid environments evident on the undersides of the leaves that have been impacted. Lesions increase as the disease proceeds, turning the leaves brown to shrivel up and die. Using picture segmentation and the Multi-class SVM technique, late blight disorder is discovered in this work. Image segmentation is employed for separating damaged areas on leaves, and the Multi-class SVM method is used for reliable disease categorization. 30 reputable studies were chosen from a total of 2770 recognized papers. The primary goal of this study is to compile cutting-edge research that identifies current research trends, problems, and prospects for late blight detection. It also looks at current approaches for applying image processing to diagnose and detect late blight. A suggested taxonomy for late blight detection has also been provided. In the same way, a model for the development of the solutions to problems is also presented. Finally, the research gaps have been presented in terms of open issues for the provision of future directions in image processing for the researchers.