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Open AccessArticle

Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning

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Ecodevelopment S.A., Environmental Applications, 57010 Thessaloniki, Greece
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Agroecosystem L.P., Research and Trade of Agricultural Products, 63200 Nea Moudania, Greece
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Laboratory of Animal Physiology, Dept. of Biochemistry and Biotechnology, University of Thessaly, 41500 Larissa, Greece
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Novagreen S.A., Agricultural Supplies, 58001 Giannitsa, Greece
*
Author to whom correspondence should be addressed.
Antioxidants 2020, 9(2), 156; https://doi.org/10.3390/antiox9020156
Received: 14 January 2020 / Revised: 6 February 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
In this research, a model for the estimation of antioxidant content in cherry fruits from multispectral imagery acquired from drones was developed, based on machine learning methods. For two consecutive cultivation years, the trees were sampled on different dates and then analysed for their fruits’ radical scavenging activity (DPPH) and Folin–Ciocalteu (FCR) reducing capacity. Multispectral images from unmanned aerial vehicles were acquired on the same dates with fruit sampling. Soil samples were collected throughout the study fields at the end of the season. Topographic, hydrographic and weather data also were included in modelling. First-year data were used for model-fitting, whereas second-year data for testing. Spatial autocorrelation tests indicated unbiased sampling and, moreover, allowed restriction of modelling input parameters to a smaller group. The optimum model employs 24 input variables resulting in a 6.74 root mean square error. Provided that soil profiles and other ancillary data are known in advance of the cultivation season, capturing drone images in critical growth phases, together with contemporary weather data, can support site- and time-specific harvesting. It could also support site-specific treatments (precision farming) for improving fruit quality in the long-term, with analogous marketing perspectives. View Full-Text
Keywords: antioxidant activity; machine learning; drones; precision farming antioxidant activity; machine learning; drones; precision farming
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MDPI and ACS Style

Karydas, C.; Iatrou, M.; Kouretas, D.; Patouna, A.; Iatrou, G.; Lazos, N.; Gewehr, S.; Tseni, X.; Tekos, F.; Zartaloudis, Z.; Mainos, E.; Mourelatos, S. Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning. Antioxidants 2020, 9, 156. https://doi.org/10.3390/antiox9020156

AMA Style

Karydas C, Iatrou M, Kouretas D, Patouna A, Iatrou G, Lazos N, Gewehr S, Tseni X, Tekos F, Zartaloudis Z, Mainos E, Mourelatos S. Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning. Antioxidants. 2020; 9(2):156. https://doi.org/10.3390/antiox9020156

Chicago/Turabian Style

Karydas, Christos; Iatrou, Miltiadis; Kouretas, Dimitrios; Patouna, Anastasia; Iatrou, George; Lazos, Nikolaos; Gewehr, Sandra; Tseni, Xanthi; Tekos, Fotis; Zartaloudis, Zois; Mainos, Evangelos; Mourelatos, Spiros. 2020. "Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning" Antioxidants 9, no. 2: 156. https://doi.org/10.3390/antiox9020156

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