Unverified paper record
Interpretable crop pest and disease identification based on comparative concept tree.
PloS one · 12 Mar 2026 · 10.1371/journal.pone.0343715
Abstract
Deep learning provides new methods for crop pest and disease identification and control, offering unique advantages in terms of recognition accuracy and efficiency. However, deep learning models generally lack interpretability, and their internal decision-making processes are difficult to understand. This, to some extent, undermines users' trust in the model's predictions and hinders its large-scale application in agricultural production. Therefore, improving model transparency and interpretability has become an important research direction. To address this issue, this study proposes a novel interpretable crop pest and disease identification model, the Contrastive Prototype Tree (CPTR). The model is designed around the core structure of "concept prototypes and decision tree," which builds clear prototype matching paths for each recognition result. This enables the model to not only have strong classification capability but also provide intuitive explanations. Additionally, the study introduces the SimCLR contrastive learning framework to enhance the model's ability to express deep image features. SimCLR guides the model to learn more discriminative visual features by maximizing the similarity between positive sample pairs and minimizing the similarity between negative sample pairs, thereby improving overall recognition performance. This study evaluated the model on three datasets: AppleLeaf9, Cassava, and Cashew. The experimental results show that CPTR achieves accuracies of 83.74%, 94.80%, and 96.01% on the three datasets, representing improvements of 4.12%, 0.34%, and 0.51% compared to Prototype Tree, respectively. These results indicate that the proposed model achieves the highest accuracy across different datasets, demonstrating its effectiveness.
Plant phenotyping relevance
植物葉画像から病害状態を推定する解釈可能な深層学習モデルを開発し、複数データセットで性能評価しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes a novel interpretable crop pest and disease identification model, the Contrastive Prototype Tree (CPTR).
abstractThis study evaluated the model on three datasets: AppleLeaf9, Cassava, and Cashew.
abstractThe experimental results show that CPTR achieves accuracies of 83.74%, 94.80%, and 96.01% on the three datasets
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.
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.