Weed Detection in Lily Fields Using YOLOv7 Optimized by Chaotic Harris Hawks Algorithm for Underground Resource Competition

Lilies, a key cash crop in Lanzhou, China, widely planted in coal-based fields, cultivated fields, and gardens, face significant yield and quality reduction due to weed infestation, which competes for essential nutrients, water, and light. To address this challenge, we propose an advanced weed detection method that combines symmetry-based convolutional neural networks with metaheuristic optimization. A dedicated weed detection dataset is constructed through extensive field investigation, data collection, and annotation. To enhance detection efficiency, we introduce an optimized YOLOv7-Tiny model, integrating dynamic pruning and knowledge distillation, which reduces computational complexity while maintaining high accuracy. Additionally, a novel Chaotic Harris Hawks Optimization (CHHO) algorithm, incorporating chaotic mapping initialization and differential evolution, is developed to fine-tune YOLOv7-Tiny parameters and activation functions. Experimental results demonstrate that the optimized YOLOv7-Tiny achieves a detection accuracy of 92.53% outperforming traditional models while maintaining efficiency. This study provides a high-performance, lightweight, and scalable solution for real-time precision weed management in lily fields, offering valuable insights for agricultural automation and smart farming applications.