Optimization and performance analysis of a novel automatic planting-irrigating integrated robot

• Published in: Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2025-02, Vol.239 (3), p.899-929
• Main Authors: Liu, Xunchen, Huang, Hongxun, Chen, Mingzhang, Fang, Yuan, Shu, Yuwen
• Format: Article
• Language: English
• Subjects: Algorithms; Desertification; Digital imaging; Image processing; Irrigation; Irrigation efficiency; Machine learning; Machine vision; Robots; Soil analysis; Sustainable development; Tree planting
• ISSN: 0954-4062; 2041-2983
• DOI: 10.1177/09544062241278191

Soil desertification is a severe environmental problem that seriously threatens the ecological environment and sustainable development. Traditional tree-planting methods face challenges such as insufficient labor force and low efficiency. Based on these problems, this study designed a novel automatic planting-irrigation integrated robot with automatic planting, irrigating, and monitoring functions. The robot obtains land information through laser triangle ranging technology, the camera, and other sensors. Machine vision and deep learning algorithms are applied for image processing and identification to identify the areas suitable for tree planting accurately. The drip irrigation module adopts the DHT11 digital temperature and humidity sensor control switch, which can realize efficient tree planting and watering operation, and ensure the growth of saplings and has a broad application prospect. Our design is meaningful in restraining soil desertification, maintaining the ecological environment, and realizing a sustainable economy.