Implementation of machine learning model in classification of potato leaf diseases
Keywords:
Accuracy, CNN, Random forest, SVM, XGBoost, Machine learningAbstract
In the contemporary agricultural landscape, Machine Learning (ML) has emerged as a sophisticated paradigm for the automated detection and classification of phytopathology via leaf-based image analysis. This research evaluates and contrasts the diagnostic efficacy of four distinct computational frameworks, Random Forest, XGBoost, Support Vector Machine (SVM), and Convolutional Neural Networks (CNN), specifically for the identification of potato foliage diseases. Utilizing a curated dataset encompassing 'Early Blight,' 'Late Blight,' and 'Healthy' specimens sourced from both public repositories and empirical field data, the investigation demonstrates that all four architectures achieve superior performance metrics and robust classification accuracy. The CNN with VGG16 outperformed the Random Forest, SVM, and XGBoost models in potato leaf disease classification. While SVM achieved strong and balanced performance (0.91 accuracy), and XGBoost and Random Forest produced satisfactory results (0.85 and 0.82 accuracy, respectively), the VGG16-based CNN achieved the highest overall accuracy of 0.96 with consistently superior precision, recall, and F1-scores across all classes. These findings indicate that deep learning, particularly the VGG16 architecture, provides more robust feature representation and improved discriminative capability for image-based plant disease classification task. This finding contributes to the development of a Machine Learning-based plant disease detection system in the smart agriculture sector.
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