ArticleAntioxidants (Basel, Switzerland)2020
Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning.
Article in Antioxidants (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology.Frontiers in plant science · 2022Pooled it
- Leakage-Aware Machine Learning and Deep Learning Benchmarking of Food Antioxidant Capacity Prediction on the Antioxidant Food Table.Antioxidants (Basel, Switzerland) · 2026Article
- Article
- Unleashing the power of AI in predicting the technological and phenolic maturity of pomegranates cultivated in Lebanon.Scientific reports · 2025Article
- Smart estimation of protective antioxidant enzymes' activity in savory (Satureja rechingeri L.) under drought stress and soil amendments.BMC plant biology · 2025Article
- Development of a Holistic In Vitro Cell-Free Approach to Determine the Redox Bioactivity of Agricultural Products.International journal of molecular sciences · 2023Review
- Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency.International journal of molecular sciences · 2023Review
- Evaluation of the antioxidant potency of Greek honey from the Taygetos and Pindos mountains using a combination of cellular and molecular methods.Molecular medicine reports · 2023Article
- Variation in Phytochemical, Antioxidant and Volatile Composition of Pomelo Fruit (Plants (Basel, Switzerland) · 2021Article
- The Significance of Redox Biomarkers in the Evaluation of the Antioxidant Profile In Vitro and In Vivo.Antioxidants (Basel, Switzerland) · 2021Article
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Authors and funding
12 authors.
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Abstract
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.
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Registered trials
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