{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T05:49:34Z","timestamp":1746078574584,"version":"3.37.3"},"reference-count":75,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["SICORP Program JPMJSC16H2","AIP Acceleration Research \u201cStudies of CPS platform to raise big-data-driven AI agriculture\u201d"],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using \u201creconstructed\u201d NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture.<\/jats:p>","DOI":"10.3390\/rs14051272","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T01:40:02Z","timestamp":1646617202000},"page":"1272","source":"Crossref","is-referenced-by-count":26,"title":["Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image\u2014Enhancing UAV-Based Phenotyping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3916-7388","authenticated-orcid":false,"given":"Jiangsan","family":"Zhao","sequence":"first","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]},{"given":"Ajay","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology, Hyderabad 502284, Telangana, India"}]},{"given":"Balaji Naik","family":"Banoth","sequence":"additional","affiliation":[{"name":"Agro Climate Research Center, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500030, Telangana, India"}]},{"given":"Balram","family":"Marathi","sequence":"additional","affiliation":[{"name":"Department of Genetics and Plant Breeding, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500030, Telangana, India"}]},{"given":"Pachamuthu","family":"Rajalakshmi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology, Hyderabad 502284, Telangana, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8098-0616","authenticated-orcid":false,"given":"Boris","family":"Rewald","sequence":"additional","affiliation":[{"name":"Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, 1180 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2123-4354","authenticated-orcid":false,"given":"Seishi","family":"Ninomiya","sequence":"additional","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3017-5464","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Han, W., Niu, X., and Li, G. 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