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In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.<\/jats:p>","DOI":"10.3390\/rs16020282","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T07:50:48Z","timestamp":1704873048000},"page":"282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5832-6087","authenticated-orcid":false,"given":"Brandon","family":"Victor","sequence":"first","affiliation":[{"name":"Department of Mathematical and Computer Sciences, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1352-9910","authenticated-orcid":false,"given":"Aiden","family":"Nibali","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Computer Sciences, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9841-1518","authenticated-orcid":false,"given":"Saul Justin","family":"Newman","sequence":"additional","affiliation":[{"name":"Leverhulme Centre for Demographic Science, University of Oxford, Oxford OX1 1JD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3447-973X","authenticated-orcid":false,"given":"Tristan","family":"Coram","sequence":"additional","affiliation":[{"name":"Australian Grain Technologies (AGT), Roseworthy, SA 5371, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3000-9084","authenticated-orcid":false,"given":"Francisco","family":"Pinto","sequence":"additional","affiliation":[{"name":"International Maize and Wheat Improvement Center (CIMMYT), Texcoco 56237, MX, Mexico"},{"name":"Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, 6700 AK Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4291-4316","authenticated-orcid":false,"given":"Matthew","family":"Reynolds","sequence":"additional","affiliation":[{"name":"International Maize and Wheat Improvement Center (CIMMYT), Texcoco 56237, MX, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8700-6613","authenticated-orcid":false,"given":"Robert T.","family":"Furbank","sequence":"additional","affiliation":[{"name":"Division of Plant Science, Research School of Biology, Australian National University, Acton, ACT 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0302-5775","authenticated-orcid":false,"given":"Zhen","family":"He","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Computer Sciences, La Trobe University, Melbourne, VIC 3086, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20260","DOI":"10.1073\/pnas.1116437108","article-title":"Global Food Demand and the Sustainable Intensification of Agriculture","volume":"108","author":"Tilman","year":"2011","journal-title":"Proc. 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