{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:23:10Z","timestamp":1774797790452,"version":"3.50.1"},"reference-count":110,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund within the Estonian National Programme for Addressing Socio-Economic Challenges through R&amp;D (RITA): L180283PKKK and the Doctoral School of Earth Sciences and Ecology, financed by the European Union, European Regional Dev","award":["L180283PKKK and ASTRA project \u201cValue-chain based bio-economy\u201d"],"award-info":[{"award-number":["L180283PKKK and ASTRA project \u201cValue-chain based bio-economy\u201d"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.<\/jats:p>","DOI":"10.3390\/rs14051114","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0077-3770","authenticated-orcid":false,"given":"Kai-Yun","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0758-0656","authenticated-orcid":false,"given":"Raul","family":"Sampaio de Lima","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-1608","authenticated-orcid":false,"given":"Niall G.","family":"Burnside","sequence":"additional","affiliation":[{"name":"Centre for Earth Observation, School of Applied Sciences, University of Brighton, Lewes Road, Brighton BN2 4GJ, UK"}]},{"given":"Ele","family":"Vahtm\u00e4e","sequence":"additional","affiliation":[{"name":"Estonian Marine Institute, University of Tartu, M\u00e4ealuse 14, 12618 Tallinn, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9679-1422","authenticated-orcid":false,"given":"Tiit","family":"Kutser","sequence":"additional","affiliation":[{"name":"Estonian Marine Institute, University of Tartu, M\u00e4ealuse 14, 12618 Tallinn, Estonia"}]},{"given":"Karli","family":"Sepp","sequence":"additional","affiliation":[{"name":"Agricultural Research Center, 4\/6 Teaduse St., 75501 Saku, Estonia"}]},{"given":"Victor Henrique","family":"Cabral Pinheiro","sequence":"additional","affiliation":[{"name":"Institution of Computer Science, Faculty of Science and Technology, University of Tartu, 50090 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2904-5838","authenticated-orcid":false,"given":"Ming-Der","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan"}]},{"given":"Ants","family":"Vain","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8076-7943","authenticated-orcid":false,"given":"Kalev","family":"Sepp","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1007\/s11119-017-9532-7","article-title":"New Trends in Precision Agriculture: A Novel Cloud-Based System for Enabling Data Storage and Agricultural Task Planning and Automation","volume":"18","author":"Torres","year":"2017","journal-title":"Precis. 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