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A Lightweight Framework for Protected Vegetable Disease Detection in Complex Scenes.

Jun Liu¹, Xuewei Wang¹, Qian Chen²

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

Food Science & Nutrition

|May 5, 2025

Summary

This summary is machine-generated.

This study introduces VegetableDet, an AI system for smart agriculture that accurately detects protected vegetable diseases. It enhances detection accuracy and real-time performance in complex environments, supporting efficient crop management.

Keywords:

adaptive feature enhancement deformable attention mechanism lightweight object detection protected vegetable diseases transfer learning

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Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Vegetable diseases pose a significant threat to agricultural production, impacting crop yield and quality.
Traditional manual inspection methods for disease detection are time-consuming, labor-intensive, and prone to inaccuracies.
Existing computer vision methods struggle with complex backgrounds, diverse disease symptoms, and occlusions in real-world cultivation settings.

Purpose of the Study:

To develop an intelligent system for accurate and efficient detection of protected vegetable diseases.
To overcome challenges of limited data acquisition and sample scarcity in vegetable disease identification.
To enhance the robustness and real-time performance of computer vision models in smart agriculture.

Main Methods:

Developed VegetableDet, a lightweight deep learning network integrating Deformable Attention Transformer (DAT) with YOLOv8n.
Incorporated a Channel-Spatial Adaptive Attention Mechanism (CSAAM) for precise feature localization and enhancement.
Implemented a differentiated data augmentation strategy and hierarchical progressive transfer learning for improved model training and adaptation.

Main Results:

VegetableDet achieved high performance in detecting 30 diseases and healthy samples across 5 vegetable types.
Precision (P), Recall (R), and Average Precision (AP) exceeded 90%, with a mean Average Precision (mAP) of 94.31%.
The model demonstrated strong adaptability and anti-interference capabilities in complex environmental conditions.

Conclusions:

VegetableDet provides a reliable technical solution for real-time monitoring and precise control of protected vegetable diseases.
The developed system offers significant potential for advancing smart agriculture and improving vegetable production efficiency.
The innovative combination of attention mechanisms and transfer learning strategies shows promise for future agricultural AI applications.