Automated Pruning of Greenhouse Indeterminate Tomato Plants

Abstract

Pruning of indeterminate tomato plants is vital for a profitable yield and it still remains a manual process. There has been research in automated pruning of grapevines, trees, and other plants, but tomato plants have yet to be explored. Wage increases are contributing to the depleting profits of greenhouse tomato farmers. In this paper, we investigate the feasibility of automating the process of finding greenhouse tomato pruning points. The algorithm proposed in this work can preprocess the image, classify the skeleton, calculate the intersection point statistics, identify, and mark the suckers read in the image with a 97% true positive detection rate using a dataset with 100 images.