Pea Plant Leaf Disease Detection Using ResNetV2

Early and accurate detection of pea plant leaf diseases is critical for improving crop yield and preventing widespread damage. This paper presents a deep learning-based approach for automated classification of pea leaf diseases using the ResNetV2 convolutional neural network architecture. The study utilizes a publicly available pea plant leaf dataset comprising four classes: Downy Mildew, Powdery Mildew, Leafminer damage, and Healthy leaves. The dataset was preprocessed and augmented to enhance model generalization, and the ResNetV2 model was fine-tuned to achieve effective feature extraction and classification. Experimental results demonstrate that the proposed method achieves a validation accuracy of approximately 94%, outperforming baseline models. The model’s performance was further analyzed via precision, recall, F1-score, and confusion matrix, confirming its robustness across all disease categories. The findings indicate that ResNetV2 is a promising candidate for practical deployment in agricultural monitoring systems, enabling timely disease diagnosis and management.