End-to-End Inflorescence Measurement for Supporting Table Grape Trimming with Augmented Reality

Inflorescence trimming is a crucial process to produce high-quality table grapes. It can eliminate nutrient competition in a bunch and makes it less vulnerable to disease development. After trimming, the remaining part of the inflorescence should have a target length decided by the grape variety. This is challenging for novice farmers because of the time constraint. The farmer needs to finish trimming the inflorescence before the berries develop. This paper proposes a novel end-to-end inflorescence length measurement method for supporting a trimming process with augmented reality technology. The proposed technique makes use of the state-of-the-art deep neural network model for detecting the inflorescence area, as well as the scissors from the images captured with a camera installed on an optical see-through head-mounted display. A new algorithm is designed to estimate the length of the remaining inflorescence with the screw of the scissors loop as the calibrator. The estimated length is then visualized on the head-mounted display to support the farmer in performing the trimming correctly and efficiently. The experiment, conducted with real inflorescence trimming tasks, shows that the mean absolute error of the length estimation is only 0.19 cm, which is small enough for use in real applications.