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Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure effi...

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Detalles Bibliográficos
Autores principales: Al-Naji, Ali, Fakhri, Ahmed Bashar, Gharghan, Sadik Kamel, Chahl, Javaan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841365/
https://www.ncbi.nlm.nih.gov/pubmed/33537493
http://dx.doi.org/10.1016/j.heliyon.2021.e06078
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author Al-Naji, Ali
Fakhri, Ahmed Bashar
Gharghan, Sadik Kamel
Chahl, Javaan
author_facet Al-Naji, Ali
Fakhri, Ahmed Bashar
Gharghan, Sadik Kamel
Chahl, Javaan
author_sort Al-Naji, Ali
collection PubMed
description Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10(−6) (training), 1.004 × 10(−5) (testing) and 1.809 × 10(−5) (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.
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spelling pubmed-78413652021-02-02 Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study Al-Naji, Ali Fakhri, Ahmed Bashar Gharghan, Sadik Kamel Chahl, Javaan Heliyon Research Article Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10(−6) (training), 1.004 × 10(−5) (testing) and 1.809 × 10(−5) (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture. Elsevier 2021-01-25 /pmc/articles/PMC7841365/ /pubmed/33537493 http://dx.doi.org/10.1016/j.heliyon.2021.e06078 Text en © 2021 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Al-Naji, Ali
Fakhri, Ahmed Bashar
Gharghan, Sadik Kamel
Chahl, Javaan
Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
title Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
title_full Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
title_fullStr Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
title_full_unstemmed Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
title_short Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
title_sort soil color analysis based on a rgb camera and an artificial neural network towards smart irrigation: a pilot study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841365/
https://www.ncbi.nlm.nih.gov/pubmed/33537493
http://dx.doi.org/10.1016/j.heliyon.2021.e06078
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