This thesis examines the potential of some learning-based computer vision algorithms with low data reliance on the problem of plant segmentation, namely, few-shot learning algorithms and clustering algorithm. We thoroughly investigate the mechanisms, benchmarks, and features of each algorithm. Each algorithm is applied to the plant segmentation problem. Then we show and discuss the results of each algorithm and further apply possible modifications and tuning to them. After this study, we have gathered performances for 3 different algorithms which have recall values of 54.8%, 87.7%, and 96.9% respectively.
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