New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment

Miguel Noguera (), Borja Millan and José Manuel Andújar
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Miguel Noguera: Centro de Investigación en Tecnología, Energía y Sostenibilidad (CITES), Universidad de Huelva, La Rábida, Palos de la Frontera, 21819 Huelva, Spain
Borja Millan: Departamento de Ingeniería Eléctrica, Electrónica, Informática y de Sistemas, Universidad de Oviedo, C/ Pedro Puig Adam, 33203 Gijón, Spain
José Manuel Andújar: Centro de Investigación en Tecnología, Energía y Sostenibilidad (CITES), Universidad de Huelva, La Rábida, Palos de la Frontera, 21819 Huelva, Spain

Agriculture, 2022, vol. 13, issue 1, 1-17

Abstract: The state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development and poor responsiveness for growers. These limitations have shifted the focus to remote-sensing, spectroscopy-based approaches. However, most of the research focusing on these approaches has been accomplished with expensive equipment, which is exorbitant for most users. To combat this issue, this work presents a low-cost, hand-held, multispectral device with original hardware specially designed to face the complexity related to in-field use. The proposed device is based on a development board (AS7265x, AMS AG) that has three sensor chips with a spectral response of eighteen channels in a range from 410 to 940 nm. The proposed device was evaluated in a red-grape field experiment. Briefly, it was used to acquire the spectral signature of eighty red-grape samples in the vineyard. Subsequently, the grape samples were analysed using standard chemical methods to generate ground-truth values of ripening status indicators (soluble solid content (SSC) and titratable acidity (TA)). The eighteen pre-process reflectance measurements were used as input for training artificial neural network models to estimate the two target parameters (SSC and TA). The developed estimation models were evaluated through a leave-one-out cross-validation approach obtaining promising results (R 2 = 0.70, RMSE = 1.21 for SSC; and R 2 = 0.67, RMSE = 0.91 for TA).

Keywords: sensor; multispectral; precision farming; machine learning; artificial neural network; AS7265x (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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