An autonomous Internet of Things spectral sensing system for in-situ optical monitoring of grape ripening: design, characterization, and operation
Articolo
Data di Pubblicazione:
2024
Citazione:
An autonomous Internet of Things spectral sensing system for in-situ optical monitoring of grape ripening: design, characterization, and operation / H.M. Oliveira, A. Tugnolo, N. Fontes, C. Marques, Á. Geraldes, S. Jenne, H. Zappe, A. Graça, V. Giovenzana, R. Beghi, R. Guidetti, J. Piteira, P. Freitas. - In: COMPUTERS AND ELECTRONICS IN AGRICULTURE. - ISSN 0168-1699. - 217:(2024 Jan), pp. 108599.1-108599.13. [10.1016/j.compag.2023.108599]
Abstract:
The present work proposes a novel autonomous Internet of Things (IoT) spectral sensing system for in-situ optical
monitoring of grape ripening through reflectance signals. To this end, tailor-made hardware for this IoT end node
was developed, characterized, and operated in both lab and field conditions. It included three complementary
modules: the optical module, the host module, and the controller module. The optical module included four
photodetectors and four LEDs with maximum emission wavelength centered at 530, 630, 690, and 730 nm that
was placed in direct contact with the grape berry. The host module included the LED driver and the analog front-
end for signal acquisition. Finally, the controller module provided full control of the system and ensured data
storage, power management, and connectivity. The system was capable of measuring reflectance in the range of 4
– 100 % with a linear response (r2 > 998) and with a high reproducibility among different optical units. This
design made it possible to collect reflectance signals from red (cv. Touriga Nacional) and white (cv. Loureiro)
grape varieties in both lab and field environments. The relationship between this optical fingerprint (comprised
of the different reflectance intensities recorded) and the evolution of grape berry quality parameters throughout
the ripening period (for approximately two months), was analyzed and discussed. Lab data was used to establish
a multivariate model based on Partial Least Squares for the prediction of the Total Soluble Solids (TSS) content in
both varieties. The model error (Root Mean Square Error in Cross Validation) was 2.31 and 0.73 ◦ Brix for Touriga
Nacional and Loureiro, respectively. This model was applied to data acquired in the field in an illustrative
example of the potential of the system to predict TSS in real time. The field observations collected during the
monitoring period also provided relevant information about the potential issues that may occur during the
unattended operation of the optical sensors. Additionally, the modular architecture of the optical module pro-
posed makes it possible to use different LEDs and photodetectors, as well as the assembly of optical filters. This
creates the possibility of using the same principles for measuring reflectance in different spectral ranges (e.g. IR)
or even fluorescence. The results herein described paved the work for future developments of this technology
that will include the development of prediction models for the most relevant grape ripening parameters based on
reflectance data, as well as its operation as part of a Wireless Sensor Network.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Chemometrics; Grape ripening monitoring; Precision viticulture; Proximal sensing; Wireless sensor network
Elenco autori:
H.M. Oliveira, A. Tugnolo, N. Fontes, C. Marques, Á. Geraldes, S. Jenne, H. Zappe, A. Graça, V. Giovenzana, R. Beghi, R. Guidetti, J. Piteira, P. Freitas
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