Plant Stem Segmentation Using Fast Ground Truth Generation
This work addresses plant stress analysis for agricultural research, but it is incremental as it applies existing deep learning methods to a specific domain with a new ground truth technique.
The paper tackles the problem of accurately phenotyping plant wilting by segmenting tomato plant stems using deep learning, and proposes a control-point-based ground truth method that reduces dataset creation resources, with experimental results showing viability for both approaches.
Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.