Research on High-Precision Target Detection Technology for Tomato-Picking Robots in Sustainable Agriculture

Abstract

Robotic tomato picking is a crucial step toward mechanized and precision farming. Effective tomato recognition and localization algorithms for these robots require high accuracy and real-time performance in complex field environments. This study modifies the SSD model to develop a fast and high-precision tomato detection method. The classical SSD model is optimized by discarding certain feature maps for larger objects and incorporating a self-attention mechanism. Experiments utilized images from an organic tomato farm. The model was trained and evaluated based on detection accuracy, recall rate, time consumption, and model size. Results indicate that the modified SSD model has a 95% detection accuracy and 96.1% recall rate, outperforming the classical and self-attention SSD models in accuracy, time consumption, and model size. Field experiments also demonstrate its robustness under different illumination conditions. In conclusion, this study promotes the development of tomato-picking robots by presenting an optimized detection method that effectively balances accuracy and efficiency. This method improves detection accuracy remarkably. It also reduces complexity, making it very suitable for real-world use. It plays a crucial role in facilitating the adoption of robotic harvesting systems in modern agriculture. Technologically, it remarkably boosts the picking efficiency, lessens the reliance on human labor, and cuts down fruit losses through precise picking. As a result, it effectively enhances resource utilization efficiency, providing a practical solution for the development of sustainable agriculture.