PLANT DESEASE CLASSIFICATION USING ENSEMBLE LEARNING

Plant diseases significantly impact agricultural productivity and food security worldwide. Accurate and timely identification of plant diseases is crucial for effective disease management and mitigation. Traditional disease diagnosis methods are often time-consuming and require expert knowledge, making automated solutions highly desirable. In this research, we propose a plant disease classification system using ensemble learning techniques to enhance classification accuracy and robustness. The proposed model integrates multiple machine learning algorithms, such as convolutional neural networks (CNN), random forests, and support vector machines (SVM), to improve predictive performance. A publicly available plant disease dataset containing leaf images is used to train and validate the model. The ensemble approach leverages the strengths of individual classifiers, reducing misclassification rates and improving generalization. Experimental results demonstrate that the ensemble learning model outperforms single-model classifiers in terms of accuracy, precision, and recall. The findings suggest that the proposed approach can serve as an effective tool for automated plant disease diagnosis, aiding farmers and agricultural experts in making informed decisions.