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Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms

The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Met...

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Autores principales: Espinosa-Herrera, José M., Macedo-Cruz, Antonia, Fernández-Reynoso, Demetrio S., Flores-Magdaleno, Héctor, Fernández-Ordoñez, Yolanda M., Soria-Ruíz, Jesús
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415415/
https://www.ncbi.nlm.nih.gov/pubmed/36015867
http://dx.doi.org/10.3390/s22166106
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author Espinosa-Herrera, José M.
Macedo-Cruz, Antonia
Fernández-Reynoso, Demetrio S.
Flores-Magdaleno, Héctor
Fernández-Ordoñez, Yolanda M.
Soria-Ruíz, Jesús
author_facet Espinosa-Herrera, José M.
Macedo-Cruz, Antonia
Fernández-Reynoso, Demetrio S.
Flores-Magdaleno, Héctor
Fernández-Ordoñez, Yolanda M.
Soria-Ruíz, Jesús
author_sort Espinosa-Herrera, José M.
collection PubMed
description The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season
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spelling pubmed-94154152022-08-27 Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms Espinosa-Herrera, José M. Macedo-Cruz, Antonia Fernández-Reynoso, Demetrio S. Flores-Magdaleno, Héctor Fernández-Ordoñez, Yolanda M. Soria-Ruíz, Jesús Sensors (Basel) Article The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season MDPI 2022-08-16 /pmc/articles/PMC9415415/ /pubmed/36015867 http://dx.doi.org/10.3390/s22166106 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Espinosa-Herrera, José M.
Macedo-Cruz, Antonia
Fernández-Reynoso, Demetrio S.
Flores-Magdaleno, Héctor
Fernández-Ordoñez, Yolanda M.
Soria-Ruíz, Jesús
Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
title Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
title_full Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
title_fullStr Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
title_full_unstemmed Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
title_short Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
title_sort monitoring and identification of agricultural crops through multitemporal analysis of optical images and machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415415/
https://www.ncbi.nlm.nih.gov/pubmed/36015867
http://dx.doi.org/10.3390/s22166106
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