Authors: Hugo M. Oliveira | Alessio Tugnolo | Natacha Fontes | C. N. Marques | Álvaro Geraldes | Sophie Jenne | Hans Zappe | António Graça | Valentina Giovenzana | R. Beghi | R. Guidetti | João Piteira | P. P. Freitas
Source: Computers and Electronics in Agriculture (article, 2024-01-06)
Field: Chemistry — Analytical Chemistry

An autonomous Internet of Things spectral sensing system for in-situ optical monitoring of grape ripening: design, characterization, and operation 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 proposed 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. Ripening ; Controller (irrigation) ; Interface (matter) ; Engineering ; Computer science ; Horticulture Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Listeria monocytogenes in Food Safety