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Tea harvesting is an operation in which the tender tea shoots (buds) are pickled, which is generally termed as "plucking". In order the make a plucking decision, counting correctly the maturity of tea shoots is extremely important because it determines the quality of tea production. However, it is a tedious task and takes large amount of time of tea producers. In this work, we show how computer vision can be used to improve the manual method. Our automatic system requires only the images acquired from a tea field in order the count tea shoots.
Motivation & Objective
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.
A training set of tea shoot
Clustering detected features
|Defected features||Cluster results|
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.