2020/01/24 by Changye Yang, Yang, Changye, Sriram Baireddy +14
Agricultural and Biological Sciences · Computer Science · Environmental Science · Mathematics · #Agronomy #Artificial intelligence #Biology #Botany #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #FOS: Computer and information sciences #Ground truth #Leaf Properties and Growth Measurement #Mathematics #Permanent wilting point #Point (geometry) #Remote Sensing in Agriculture #Segmentation #Smart Agriculture and AI #Wilting #cs.CV
paper · pdf · doi:10.48550/arxiv.2001.08854
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/01/24 · openalex publication_date 2020/01/24 · arxiv updated 2020/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.