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DAPR-AM-Net: an end-to-end smart farming system powered by dual-attention progressive refinement and adaptive MixUp
1School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, Shandong, China.
Plant Methods
|June 28, 2026
Summary
This study introduces DAPR-AM-Net, an AI framework for diagnosing tomato leaf diseases. It achieves high accuracy by integrating dual-attention mechanisms and adaptive MixUp for improved stability and interpretability in smart agriculture.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Plant Pathology
Background:
- Smart agriculture relies on vision-based crop disease diagnosis.
- Challenges include complex backgrounds, similar lesion appearances, and imbalanced data, hindering model performance and interpretability.
Purpose of the Study:
- To develop an intelligent diagnostic framework, DAPR-AM-Net, for tomato leaf diseases.
- To enhance model stability, interpretability, and accuracy in challenging agricultural environments.
Main Methods:
- Proposed DAPR-AM-Net framework integrating Dual Attention Fusion Mechanism (DAFM) and Adaptive MixUp with Attention-Aware Sampling (AMAAS).
- Incorporated Progressive Feature Refinement with Dual Attention (PFR-DA) and Imbalance-Aware Multi-Objective Optimization (IAMOO).
- Utilized CBAM module for attention-based noise suppression and feature enhancement.
Main Results:
- DAPR-AM-Net achieved 99.73% accuracy on the Tomato-DD dataset and 99.85% on the Plant-Village dataset.
- Demonstrated superior performance in precision, recall, and F1-score compared to state-of-the-art methods.
- Maintained a compact model size (4.72M parameters) with high accuracy and interpretability.
Conclusions:
- DAPR-AM-Net offers a practical and innovative solution for accurate, interpretable disease diagnosis in smart agriculture.
- The framework shows significant potential for real-world application in precision agriculture platforms.
- The study highlights the effectiveness of attention mechanisms and adaptive learning strategies in addressing data challenges.
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