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Article

Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology

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Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo 93022-750, RS, Brazil
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Department of Engineering, Architecture and Computing, University of Santa Cruz do Sul, Av. Independencia 2293, Santa Cruz do Sul 96815-900, RS, Brazil
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Industrial Systems and Processes Graduate Program, University of Santa Cruz do Sul, Av. Independencia 2293, Santa Cruz do Sul 96815-900, RS, Brazil
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Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
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COPELABS, University Lusófona—ULHT, 1749-024 Lisbon, Portugal
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VALORIZA—Research Centre for Endogenous Resource Valorization, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Jameela Al-Jaroodi, Sanja Lazarova-Molnar and Nader Mohamed
J. Sens. Actuator Netw. 2021, 10(3), 40; https://doi.org/10.3390/jsan10030040
Received: 25 May 2021 / Revised: 22 June 2021 / Accepted: 23 June 2021 / Published: 25 June 2021
The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences. View Full-Text
Keywords: machine learning; multispectral image; soil; clay; agriculture machine learning; multispectral image; soil; clay; agriculture
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MDPI and ACS Style

Helfer, G.A.; Barbosa, J.L.V.; Alves, D.; da Costa, A.B.; Beko, M.; Leithardt, V.R.Q. Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. J. Sens. Actuator Netw. 2021, 10, 40. https://doi.org/10.3390/jsan10030040

AMA Style

Helfer GA, Barbosa JLV, Alves D, da Costa AB, Beko M, Leithardt VRQ. Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. Journal of Sensor and Actuator Networks. 2021; 10(3):40. https://doi.org/10.3390/jsan10030040

Chicago/Turabian Style

Helfer, Gilson Augusto, Jorge Luis Victória Barbosa, Douglas Alves, Adilson Ben da Costa, Marko Beko, and Valderi Reis Quietinho Leithardt. 2021. "Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology" Journal of Sensor and Actuator Networks 10, no. 3: 40. https://doi.org/10.3390/jsan10030040

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