Pea Plant Leaf Disease Detection Using ResNetV2

Abstract

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.

Plant phenotyping relevance

植物葉の画像から病害状態を分類する深層学習手法が研究の中心であり、植物病害の表現型推定に該当する。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.