Crop Detection Method Using Relative Positional Relationships for Small Weeding Robots

Automation in agriculture is increasingly critical for addressing global food security and sustainability challenges. This paper presents a novel crop detection method using relative positional relationships, specifically designed for small weeding robots. Unlike traditional approaches that rely heavily on visual characteristics and large annotated datasets, our method leverages the spatial arrangement of plants to distinguish crops from weeds, thereby reducing the dependency on extensive data collection and annotation efforts. We implemented a multi- stage detection system that first identifies all plants using an object detection algorithm and then classifies them based on their positional and size information. Experimental results on soybean datasets demonstrate that our approach achieves AP of 71.3% for soy crops and 27.2% for weeds in environments not included in the training dataset, showing comparable effectiveness to traditional visual-based detection methods in scenarios with limited data. This advancement offers potential for enhancing the adaptability and efficiency of agricultural automation technologies.