Robust Leaf Disease Detection Using Complex Fuzzy Sets and HSV-Based Color Segmentation Techniques
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2024, Vol 3, Issue 3
Abstract
Leaf diseases pose a significant threat to global agricultural productivity, impacting both crop yields and quality. Traditional detection methods often rely on expert knowledge, are labor-intensive, and can be time-consuming. To address these limitations, a novel framework was developed for the segmentation and detection of leaf diseases, incorporating complex fuzzy set (CFS) theory and advanced spatial averaging and difference techniques. This approach leverages the Hue, Saturation, and Value (HSV) color model, which offers superior contrast and visual clarity, to effectively distinguish between healthy and diseased regions in leaf images. The HSV space was utilized due to its ability to enhance the visibility of subtle disease patterns. CFSs were introduced to manage the inherent uncertainty and imprecision associated with disease characteristics, enabling a more accurate delineation of affected areas. Spatial techniques further refine the segmentation, improving detection precision and robustness. Experimental validation on diverse datasets demonstrates the proposed method’s high accuracy across a variety of plant diseases, highlighting its reliability and adaptability to real-world agricultural conditions. Moreover, the framework enhances interpretability by offering insights into the progression of disease, thus supporting informed decision-making for crop protection and management. The proposed model shows considerable potential in practical agricultural applications, where it can assist farmers and agronomists in timely and accurate disease identification, ultimately improving crop management practices.
Authors and Affiliations
Ibrar Hussain, Rifaqat Ali
Keywords
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