Multispectral Cameras and Machine Learning integrated into Portable Devices as Enabling Technology for Smart Farms

Gilson Augusto Helfer; Jorge Luis Victoria Barbosa; Douglas Alves; Adilson Ben da Costa; Marko Beko; Valderi Reis Quietinho Leithardt

doi:10.20944/preprints202105.0630.v1

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preprints.org > computer science and mathematics > information systems > doi: 10.20944/preprints202105.0630.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multispectral Cameras and Machine Learning integrated into Portable Devices as Enabling Technology for Smart Farms

Gilson Augusto Helfer

^* ,

Jorge Luis Victoria Barbosa

Douglas Alves

Adilson Ben da Costa

Marko Beko

Valderi Reis Quietinho Leithardt

Version 1 : Received: 25 May 2021 / Approved: 26 May 2021 / Online: 26 May 2021 (11:03:37 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

DOI: 10.3390/jsan10030040

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Abstract

The present work proposed a low-cost portable device as an enabling technology for Smart Farms 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 an 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.

Keywords

Smart Farm; Smart Systems; Machine Learning; Multispectral Image; Clay.

Subject

Computer Science and Mathematics, Information Systems

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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