Development of Autonomous Sprayer Considering Tracking Performance on Geometrical Complexity of Ground in Greenhouse

Abstract

Purpose: Some of the most representative approaches are to apply next generation technologies to save energy consumption, fully automated control system to appropriately maintain environmental conditions, and autonomous assistance system to reduce labor load and ensure operator's safety. Nevertheless, improvement of upcoming method for soil cultured greenhouse has not been sufficiently achieved. Geometrical complexity of ground in protected crop cultivation might be one of the most dominant factors in design of autonomous vehicle. While there is a practical solution fairly enough to promise an accurate travelling, such as autonomous sprayer guided by rail or induction coil, for various reasons including the limitation of producer's budget, the previously developed sprayer has not been widely distributed to market. Methods: In this study, we developed an autonomous sprayer considering travelling performance on geometrical complexity of ground in soil cultured greenhouse. To maintain a stable travelling and to acquire a real time feedback, common wire with 80 mm thick and body frame and sprayer boom. To evaluate performance of the prototype, tracking performance, climbing performance and spraying boom's uniform leveling performance were individually evaluated by corresponding experimental tests. Results: The autonomous guidance system was proved to be sufficiently suitable for accurate linear traveling with RMS as lower than approximately 10 cm from designated path. Also the prototype could climb 10° of ground's slope angle with 40 kg of water weight. Uniform leveling of spraying boom was successfully performed within 0.5° of sprayer boom's slope. Conclusions: Considering more complex pathways and coarse ground conditions, evaluations and improvements of the prototype should be performed for promising reliability to commercialization.

Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

“…In the spraying system, a machine learning system is required to discern spraying spots and non-spraying spots in operational environments of UAVs, prior to implementing an autonomous spraying system. Ground-based vehicles can function as image processing systems, with advantages for housing onboard sprayers [8,9]. However, ground vehicles have local mapping systems without predetermined field coverage.…”