Optimizing Parameter Efficiency in Machine Learning Models: A Focus on Reducing Memory Overhead with L-BFGS-Optimized Algorithms

Abstract

This research addresses the critical challenge of detecting cotton leaf diseases through an advanced image processing and machine learning approach. The study primarily focuses on the extraction of significant features from cotton leaf images to facilitate accurate disease identification. The novelty of this work lies in the application of the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for optimizing Support Vector Machines (SVM), enhancing the precision of disease detection in cotton leaves. This research method involves a comprehensive process of acquiring high-resolution leaf images, followed by an effective feature extraction technique that isolates crucial characteristics indicative of various plant diseases. Subsequently, these features are employed to train an SVM model optimized by the L-BFGS algorithm, a decision process that marks a significant departure from traditional gradient descent methods in SVM training. A pivotal aspect of this study is the comparative analysis conducted between the proposed L-BFGS-optimized SVM model and prevalent machine learning models including Random Forest, Decision Tree, Regression Models, and K-Nearest Neighbors (KNN). This comparison is grounded on various performance metrics such as accuracy, precision, recall, and F1-score, offering a comprehensive evaluation of each model's effectiveness in disease detection. The results of this research are expected to demonstrate the superiority of the L-BFGS-optimized SVM in terms of accuracy and efficiency in detecting cotton leaf diseases. This advancement holds substantial promise for agricultural technology, potentially leading to more informed and timely decision-making in crop management and disease prevention. The findings of this study aim to contribute significantly to the field of agricultural informatics, particularly in the domain of plant disease detection and management, by providing a more robust, accurate, and efficient tool for farmers and agriculturalists.