Unified Learning Ensemble for Robust Leaf Disease Detection: Integrating Improved Laplacian of Gaussian (ILoG) and Unified Learning Ensemble Approach

The world's population mostly obtains its food from plants. Plant diseases, which may be prevented with continuous observation, are a contributing factor in productivity loss. Monitoring plant diseases manually is time-consuming and prone to mistakes. To overcome this problem, this research endeavours to develop an advanced tomato leaf disease detection system, aiming to address the pressing challenge of crop health deterioration. The methodology involves a comprehensive approach, starting with a robust pre-processing pipeline utilizing Non-Local Means (NLM) for image cleaning, standardizing image sizes, and normalizing pixel values to enhance model convergence. A novel Improved Laplacian of Gaussian (ILoG) segmentation algorithm is proposed for precise leaf region extraction, while advanced texture analysis methods such as Gamma Correlation-based Local Phase Quantization (GC-LPQ) and Local Directional Patterns (LDP) are explored for feature extraction. Additionally, colour-based features, including colour moments, and geometric features like leaf area, perimeter, and shape descriptors, are extracted. The research delves into effective feature selection using the Hybrid FoxWheel Optimization Algorithm (HFWOA), a combination of Binary Waterwheel Plant Optimization Algorithm (BWPOA) and FOX-Inspired Optimization Algorithm (FIOA). The Unified Learning Ensemble (ULE) approach integrates Decision Trees, Support Vector Machines (SVM), and Neural Networks with weighted voting for accurate leaf disease classification. The methodology's efficacy is evaluated through cross-validation, achieving an accuracy of 99.29% with an 80:20 training-testing split. The implementation is publicly available in a GitHub repository https://github.com/psaranyadevi27-ui/UNIFIED-LEARNING-ENSEMBLE-FORROBUST-LEAF-DISEASE-DETECTION