{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T17:43:35Z","timestamp":1774806215569,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks\u2019 potential to predict plants\u2019 expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning\/domain adaptation) on different plant species and mutations.<\/jats:p>","DOI":"10.3390\/rs13030331","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T12:16:18Z","timestamp":1611144978000},"page":"331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Predicting Plant Growth from Time-Series Data Using Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1673-1946","authenticated-orcid":false,"given":"Robail","family":"Yasrab","sequence":"first","affiliation":[{"name":"Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK"}]},{"given":"Jincheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK"}]},{"given":"Polina","family":"Smyth","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK"}]},{"given":"Michael P.","family":"Pound","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1111\/jipb.12434","article-title":"Improving crop nutrient efficiency through root architecture modifications","volume":"58","author":"Li","year":"2015","journal-title":"J. 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