An Innovative Machine Learning Framework for Precise Quantification of Leaf Disease Severity via Proposed Hybrid Segmentation and Color Manipulation Technique

Background: Precise quantification of leaf disease severity is crucial for effective plant pathology and accurate disease management.Existing segmentation techniques, including deep learningbased methods, face challenges with noise, artifacts, and occlusions in leaf images, leading to unreliable segmentation and severity assessments.Additionally, visually similar symptoms across different diseases make accurate differentiation challenging, especially when models lack training on diverse datasets.Complex backgrounds in infected leaf images further exacerbate these limitations.Objective: This study proposes a novel segmentation and quantification approach to overcome the limitations of existing methods, accurately extracting lesion areas and quantifying both lesion and healthy leaf pixels to measure infection severity.Methods: The proposed approach integrates four distinct algorithms.Algorithm 1 utilizes Adaptive K-Means Clustering to create a background mask, effectively isolating leaf pixels.Algorithm 2 applies Gaussian Mixture Models (GMM) in the Lab* color space to refine the foreground mask, accurately identifying lesion regions.Algorithm 3 employs advanced color manipulation for precise lesion delineation, while Algorithm 4 combines the outputs to determine the total pixel count of leaf and lesion areas, enabling a reliable estimation of disease severity. Results:The method was validated on a dataset of 50 images, including tomato, paddy, cucumber, and wheat leaves affected by Late Blight, Brown Spot, Downy Mildew, Septoria, and Stripe Rust.Metrics like Dice Coefficient, F1-Score, Jaccard Index, and Latency assessed segmentation quality, while Mean Absolute Error (MAE) and correlation evaluated disease severity quantification.The proposed approach outperformed state-of-the-art techniques, demonstrating superior accuracy. Conclusion:The improved precision achieved with the proposed method supports more reliable disease assessment, aiding in the development of effective crop protection strategies and enhancing decisionmaking in plant disease management.