Deep Learning Models for Precision Agriculture: Evaluating CNN Architectures for Accurate Plant Disease Detection

Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based Convolutional Neural Network (CNN) for automated plant disease detection. Four prominent CNN architectures—ResNet-50, AlexNet, GoogLeNet, and VGG—are trained and optimized using a labeled dataset of pepper bell plant images. The performance of the models is assessed based on standard evaluation metrics, including accuracy, precision, recall and F1-score. Among the evaluated architectures, ResNet-50 achieves the highest accuracy of 99.01%, demonstrating its effectiveness in differentiating between healthy and diseased plants. The results highlight the potential of deep learning models for real-time, automated agricultural diagnostics, advancing precision farming and sustainable agriculture.