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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Interpretable crop pest and disease identification based on comparative concept tree.

Bingjing Jia¹, Zhiwei Zheng¹, Jinyu Zeng¹

¹Anhui Science and Technology University, Bengbu, China.

|March 12, 2026

Summary

This study introduces the Contrastive Prototype Tree (CPTR), an interpretable deep learning model for crop pest and disease identification. CPTR enhances recognition accuracy and user trust through transparent decision-making, improving agricultural applications.

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Deep learning models offer high accuracy and efficiency in crop pest and disease identification but often lack interpretability, hindering user trust and adoption in agriculture.
Improving the transparency and interpretability of these models is crucial for their widespread application in agricultural production.

Purpose of the Study:

To propose a novel interpretable deep learning model for crop pest and disease identification.
To enhance model transparency and build user trust by providing intuitive explanations for recognition results.

Main Methods:

Developed the Contrastive Prototype Tree (CPTR) model, integrating concept prototypes and a decision tree structure for clear matching paths.
Utilized the SimCLR contrastive learning framework to improve the learning of deep image features, enhancing recognition performance.

Main Results:

CPTR achieved high accuracies of 83.74% on AppleLeaf9, 94.80% on Cassava, and 96.01% on Cashew datasets.
Demonstrated accuracy improvements of 4.12%, 0.34%, and 0.51% over the standard Prototype Tree model across the evaluated datasets.

Conclusions:

The proposed CPTR model effectively balances classification capability with interpretability, offering intuitive explanations for its predictions.
CPTR shows superior performance and effectiveness across multiple datasets, highlighting its potential for practical use in agricultural pest and disease identification.