Optimized Machine Learning System for Identifying Plant Diseases

Plant diseases pose significant threats to agriculture, adversely affecting both crop yield and quality. This study offers a comprehensive overview of plant pathology, examining various types of diseases, their causative agents, and the intricate interactions between plants and pathogens. This study explored the integration of advanced deep learning and machine learning techniques. A dataset of plant leaf diseases, sourced from an online repository, was augmented with additional data featuring 11 West African plant species. The dataset underwent rigorous preprocessing to ensure compatibility with machine learning models. This study employed the ResNet50 Convolutional Neural Network (CNN) for feature extraction and XGBoost for classification, achieving a remarkable accuracy of 98.81% in differentiating between healthy and diseased plant leaves. The performance of the developed model was evaluated using key metrics, including accuracy, precision, recall, F1-score, confusion matrix, and ROC curve, and was found to outperform existing models in terms of accuracy. Furthermore, the model was successfully integrated into a mobile application, demonstrating efficient performance. This approach presents a scalable solution for precision agriculture, enhancing crop health management and boosting agricultural productivity.

Feature Extraction, XGBoost, ResNet50, hybrid, mobile application

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