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Open AccessArticle

Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters

1
NIBIO—Norwegian Institute of Bioeconomy Research, P.O. Box 115, N-1431 Ås, Norway
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Norwegian Institute of Bioeconomy Research, Postvegen 213, Særheim, N-4353 Klepp Station, Norway
3
Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, 1190 Vienna, Austria
4
Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3258; https://doi.org/10.3390/rs12193258
Received: 19 August 2020 / Revised: 23 September 2020 / Accepted: 30 September 2020 / Published: 7 October 2020
Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond. View Full-Text
Keywords: hyperspectral image reconstruction; RGB image; deep learning; HSCNN-R; SSC; TTA; STR; lycopene; tomato hyperspectral image reconstruction; RGB image; deep learning; HSCNN-R; SSC; TTA; STR; lycopene; tomato
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Zhao, J.; Kechasov, D.; Rewald, B.; Bodner, G.; Verheul, M.; Clarke, N.; Clarke, J.L. Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters. Remote Sens. 2020, 12, 3258.

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