Rice Leaf Disease Classification with Machine Learning: An Approach Using Nu-SVM
Abstract
This study explores the application of machine learning for classifying rice leaf diseases, employing the Nu-Support Vector Machine (Nu-SVM) algorithm, analyzed through a 5-fold cross-validation approach. The research focuses on distinguishing between healthy leaves and those affected by BrownSpot and LeafBlast diseases. The dataset, comprising segmented rice leaf images processed using Sobel edge detection and Hu Moments feature extraction, is utilized to train and test the model. Results indicate a moderate level of accuracy (52.12% to 53.81%) across the cross-validation folds, with precision and recall metrics demonstrating variability and highlighting the challenges in precise disease classification. Despite this, the model maintains a consistent ability to identify true positives. The study contributes to the field of precision agriculture by showcasing the potential and limitations of using machine learning for plant disease diagnosis. It underscores the need for advanced image processing techniques and diverse feature extraction methods to enhance model performance. The findings are pivotal for developing more effective, automated diagnostic tools in agriculture, thereby aiding in better disease management and potentially improving crop yields. This research serves as a foundational step towards integrating machine learning in agricultural disease detection, emphasizing its importance in sustainable farming practices.
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