Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards

Abstract

AbstractGreen shoot thinning in vineyards is an essential, perennial operation for maintaining canopy health and optimizing yield and quality of wine grapes. Use of mechanized thinning system, which is essential to reduce labor dependency and associated cost, causes high variability in shoot removal efficiency due to difficulty in precisely positioning the thinning end‐effector along cordon trajectories. Automated/robotic solution for precise positioning of the thinning end‐effector could significantly improve the performance and efficiency of mechanical green shoot thinning. This study presents: (i) a machine vision‐based cordon detection system that can estimate cordon trajectories at different shoot growth stages in a vineyard; and (ii) evaluation of an integrated green shoot thinning system capable of automatically positioning the thinning end‐effector following vine cordon trajectories. The developed machine vision system uses deep learning‐based techniques that could accurately estimate cordon trajectories with root mean square error (RMSE) of 7.3, 10.3, and 16.1 pixels for canopy images captured in 2–4 weeks of shoot growth, respectively. Then, a control strategy was presented for the integrated system, which receives the computed cordon trajectories from machine vision system to automatically position the thinning end‐effector to cordon trajectories. Field evaluations in a research vineyard showed that the integrated system can achieve an RMSE of 1.47 cm in following the cordon trajectories at 6.6 cm·s−1forward speed. Future work will incorporate additional sensing system to detect individual shoot on cordon and integrating it with an existing system to achieve higher level of precision in green shoot thinning.