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

Abstract

Accurate vegetable disease detection in complex cultivation environments remains challenging because early lesions are often small, low-contrast, and easily confounded by cluttered backgrounds. To address this issue, we propose VDD-Net, a feature-enhanced detection network based on YOLOv10 for robust vegetable disease detection in protected agriculture. The proposed framework integrates three modules: a receptive field enhancement (RFE) module to improve local perception of small lesions, an adaptive channel fusion (ACF) module to strengthen multi-scale feature aggregation and suppress background interference, and a global context attention (GCA) module to capture long-range dependencies and improve contextual discrimination. Experiments on a custom vegetable disease dataset showed that VDD-Net achieved an mAP@0.5 of 95.2% with only 7.78 M parameters. To further evaluate robustness, zero-shot cross-domain testing was conducted on the PlantDoc dataset, where VDD-Net achieved an mAP@0.5 of 76.5%, outperforming the baseline and showing improved generalization to natural scenes. In addition, after TensorRT optimization and FP16 quantization, the model maintained real-time inference on edge platforms, reaching 89.3 FPS on Jetson AGX Orin and 24.2 FPS on Jetson Nano. These results indicate that VDD-Net provides a practical balance among detection accuracy, cross-domain robustness, and deployment efficiency for intelligent disease monitoring in modern agriculture.

Plant phenotyping relevance

野菜の病斑を直接検出する画像ベース手法を開発し、データセット・クロスドメイン性能・エッジ実装を評価しており、植物病害状態の取得方法が中心である。

Code and data availability

The paper's core assets are the custom Protected Vegetable Disease Dataset (PVDD, 30,000 annotated greenhouse images) and the VDD-Net model/code, but no public repository, deposit, or authors' URL is provided. The Data Availability Statement only offers the data upon reasonable request, so no public or actionable asset

No evidence-backed public reproduction asset is currently recorded.