Dynamic coding network for robust fruit detection in low-visibility agricultural scenes

Accurate fruit detection under low-visibility conditions such as fog, rain, and low illumination is crucial for intelligent orchard management and robotic harvesting. However, most existing detection models experience significant performance degradation in these visually challenging environments.

This study proposes a modular detection framework named Dynamic Coding Network (DCNet), designed specifically for robust fruit detection in low-visibility agricultural scenes. DCNet comprises four main components: a Dynamic Feature Encoder for adaptive multi-scale feature extraction, a Global Attention Gate for contextual modeling, a Cross-Attention Decoder for fine-grained feature reconstruction, and an Iterative Feature Attention mechanism for progressive feature refinement.

Experiments on the LVScene4K dataset, which contains multiple fruit categories (grape, kiwifruit, orange, pear, pomelo, persimmon, pumpkin, and tomato) under fog, rain, low light, and occlusion conditions, demonstrate that DCNet achieves 86.5% mean average precision and 84.2% intersection over union. Compared with state-of-the-art methods, DCNet improves F1 by 3.4% and IoU by 4.3%, maintaining a real-time inference speed of 28 FPS on an RTX 3090 GPU.

The results indicate that DCNet achieves a superior balance between detection accuracy and computational efficiency, making it well-suited for real-time deployment in agricultural robotics. Its modular architecture also facilitates generalization to other crops and complex agricultural environments.