Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

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.

Journal reference: J. Sens. Actuator Netw. 2021, 10, 40
DOI: 10.3390/jsan10030040

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

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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