Interpretable crop pest and disease identification based on comparative concept tree
Bingjing Jia, Zhiwei Zheng, Jinyu Zeng, Lei Shi, Hua Ge, Chenguang Song, Tomo Popovic, Tomo Popovic, Tomo Popovic, Tomo Popovic, Tomo Popovic
TL;DR
This paper introduces an interpretable deep learning model for identifying crop pests and diseases, improving both accuracy and transparency.
Contribution
A novel interpretable model called Contrastive Prototype Tree (CPTR) is proposed, combining concept prototypes and contrastive learning for better performance and explainability.
Findings
CPTR achieved 83.74% accuracy on the AppleLeaf9 dataset, outperforming Prototype Tree by 4.12%.
The model showed 94.80% and 96.01% accuracy on Cassava and Cashew datasets, with slight improvements over existing methods.
The use of SimCLR contrastive learning enhanced the model's ability to learn discriminative visual features.
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…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsSmart Agriculture and AI · Advanced Neural Network Applications · Phytoplasmas and Hemiptera pathogens