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Updated: May 5, 2026

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A Feature-Enhanced Network for Vegetable Disease Detection in Complex Environments.

Xuewei Wang¹, Jun Liu¹

¹Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China.

Plants (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

VDD-Net enhances vegetable disease detection using a feature-enhanced YOLOv10 network. This robust system improves accuracy in complex agricultural settings, offering practical solutions for intelligent disease monitoring.

Keywords:

deep learning feature fusion object detection vegetable disease detection

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate vegetable disease detection is crucial for crop yield and quality.
Challenges include small, low-contrast lesions and cluttered backgrounds in cultivation environments.

Purpose of the Study:

To develop a robust and efficient deep learning model for vegetable disease detection.
To improve the accuracy and generalization capabilities of disease detection systems in protected agriculture.

Main Methods:

Proposed VDD-Net, a feature-enhanced detection network based on YOLOv10.
Integrated receptive field enhancement (RFE), adaptive channel fusion (ACF), and global context attention (GCA) modules.
Evaluated on a custom dataset and the PlantDoc dataset, with TensorRT optimization and FP16 quantization.

Main Results:

Achieved 95.2% mAP@0.5 on a custom dataset with 7.78 M parameters.
Demonstrated strong cross-domain generalization with 76.5% mAP@0.5 on the PlantDoc dataset.
Real-time inference achieved on edge platforms (89.3 FPS on Jetson AGX Orin, 24.2 FPS on Jetson Nano).

Conclusions:

VDD-Net offers a practical balance of accuracy, robustness, and efficiency for intelligent vegetable disease monitoring.
The feature-enhanced network effectively addresses challenges of small lesions and complex backgrounds.
The model shows significant potential for deployment in modern agricultural practices.