A vision-based method for automatizing tea shoots detection

Counting tender tea shoots in a sampled area is required before making a decision for plucking. However, it is a tedious task and requires a large amount of time. In this paper, we propose a vision-based method for automatically detecting and counting the number of tea shoots in an image acquired from a tea field. First, we build a parametric model of a tea-shoot´s color distribution in order to roughly separate Regions-of-Interest (ROIs) of tea shoots from a complicated background. For each ROI, we then extract supportive (local) features with expectations that these features will only appear around an apical bud of tea shoots thanks to two measurements: the density of edge pixels and a statistic of gradient directions. Consequently, the extracted features are put into a mean shift cluster to locate the position of tea shoots. The proposed method is evaluated on a set of testing images with different species of tea plants and ages. The results show 86% correct tea shoots detected, whereas 25% of a false alarm rate exists. It offers an elegant way to build an assisting tool for tea harvesting.