Lee, Unseok Chang, Sungyul Putra, Gian Anantrio Kim, Hyoungseok Kim, Dong Hwan 2024-01-19T23:01:20Z 2024-01-19T23:01:20Z 2021-09-03 2018-04-27 1932-6203 https://pubs.kist.re.kr/handle/201004/121455 A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily. English PUBLIC LIBRARY SCIENCE DIGITAL REPEAT PHOTOGRAPHY ARABIDOPSIS-THALIANA ROSETTE AREA GROWTH SUPERPIXEL RESPONSES NETWORKS An automated, high-throughput plant phenotyping system using machine learning based plant segmentation and image analysis Article 10.1371/journal.pone.0196615 1 PLOS ONE, v.13, no.4 PLOS ONE 13 4 scie scopus 000431013300044 2-s2.0-85046090613 Multidisciplinary Sciences Science & Technology - Other Topics Article DIGITAL REPEAT PHOTOGRAPHY ARABIDOPSIS-THALIANA ROSETTE AREA GROWTH SUPERPIXEL RESPONSES NETWORKS high throughput plant phenotyping sensor-to-plant image acquisition system large-scale image dataset processing plant segmentation machine learning plant growth tracking