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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-7841365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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