Inferring ethylene temporal and spatial distribution in an apple orchard ( Malus domestica Borkh): a pilot study for opt
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Inferring ethylene temporal and spatial distribution in an apple orchard (Malus domestica Borkh): a pilot study for optimal sampling with a gas sensor João Valente1 · Rodrigo Almeida2 · Lammert Kooistra2 Received: 8 April 2019 / Revised: 13 October 2020 / Accepted: 29 October 2020 © The Author(s) 2020
Abstract Apples emit a volatile organic compounds during the ripening process named ethylene, which can be used to infer the optimal harvest time. Currently, the fruit ethylene emission is assessed in controlled facilities, thus laborious and expensive. This article pioneers the study of assessing ethylene emissions in uncontrolled environments. However, understanding how the ethylene spatial temporal dynamics in an open field, its still elusive. Therefore, this paper provides a model from an (Malus domestica Borkh) apple orchard for simulation and analysis of ethylene behaviour. We demonstrate that the model is able to explain the ethylene emissions behaviour in an orchard field when subject to different wind speeds, directions and ripeness stages. Based on that we have investigated different sampling schemes—regular and random—for capturing the variability of ethylene in an orchard using an electrochemical gas sensor. These results show that a random sampling scheme performs 25% better than an equivalent regular-defined grid. Moreover, the measurements acquired locally in the rows tend to be 10% more reliable than in other locations from the orchard. Finally, the ethylene variability can be assessed with a confidence of 75% using 4 and 16 sampling points. Keywords Harvest time date · Ethylene gas simulation · Open-source software · Electrochemical gas sensor · Apple orchard
1 Introduction For farmers, determining the optimal harvesting time (OHT) is key. Harvesting immature fruits results in poor quality when ripe and higher susceptibility to mechanical damage. Harvesting overripe fruits results in soft and flavourless products, with a very short shelf-life (Kader 1999).
Communicated by Youn Young Hur, Ph.D. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13580-020-00316-9) contains supplementary material, which is available to authorized users. * João Valente [email protected] 1
Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
Laboratory of Geo‑Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
2
Maturity indices exist to support farmers decision making across the growing season, such as for finding the OHT. Maturity indices can be obtained via destructive and nondestructive methods and take into account chemical composition (e.g., total acidity), physical properties [e.g., firmness (Zhang et al. 2008)], fruit physiological changes [e.g., ethylene emission rate (Kathirvelan and Vijayaraghavan 2017)], and chronological features [e.g., days after blooming (Knee 2002)]. Destructive methods are methods which the fruits or vegetable are su
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