An Efficient Computer Vision-Based Dual-Face Target Precision Variable Spraying Robotic System for Foliar Fertilisers

Abstract

The application of foliar fertiliser can rapidly replenish the essential nutrients required by crops. In order to enhance the precision of foliar fertiliser spraying, fertiliser utilisation, and leaf absorption efficiency, this study proposes the implementation of an efficient foliar fertiliser dual-face target precision variable spraying robot system based on computer vision. In this study, we propose the SN-YOLOX Nano-ECA as a real-time classification model for potted plants. The model has parameters and FLOPs of only 0.48 M and 0.16 G, respectively. Following deployment, the classification precision and recall reached 97.86% and 98.52%, respectively, with an FPS of 37.6. A dual-face target precision variable spraying method of foliar fertiliser based on the determination of leaf area and plant height information of potted plants was proposed. A robot platform for the application of foliar fertilisers was developed, and a positioning and navigation system based on the RSSI principle was constructed. The results of the foliar fertiliser spraying experiments demonstrate that the precision of the extracted leaf area and height information is above 97% and 96%, respectively. The navigation system demonstrated distance and angle errors of only 5.598 cm and 0.2245°. The mean discrepancy between the actual and set spraying volumes was 0.46 mL. This robotic system is capable of precise spraying of foliar fertiliser, which provides a new idea and reference for the development of efficient and precise variable spraying technology for foliar fertiliser.