Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically moni...

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Autores principales:	Abdellatif Moussaid, Sanaa El Fkihi, Yahya Zennayi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tree canopy segmentation tree canopy classification unsupervised learning satellite images remote sensing Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/33ce09e75da54c9ca4d1a83fb016e350
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id	oai:doaj.org-article:33ce09e75da54c9ca4d1a83fb016e350
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spelling	oai:doaj.org-article:33ce09e75da54c9ca4d1a83fb016e3502021-11-25T18:03:32ZTree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms10.3390/jimaging71102412313-433Xhttps://doaj.org/article/33ce09e75da54c9ca4d1a83fb016e3502021-11-01T00:00:00Zhttps://www.mdpi.com/2313-433X/7/11/241https://doaj.org/toc/2313-433XSmart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field.Abdellatif MoussaidSanaa El FkihiYahya ZennayiMDPI AGarticletree canopy segmentationtree canopy classificationunsupervised learningsatellite imagesremote sensingPhotographyTR1-1050Computer applications to medicine. Medical informaticsR858-859.7Electronic computers. Computer scienceQA75.5-76.95ENJournal of Imaging, Vol 7, Iss 241, p 241 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tree canopy segmentation tree canopy classification unsupervised learning satellite images remote sensing Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
spellingShingle	tree canopy segmentation tree canopy classification unsupervised learning satellite images remote sensing Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95 Abdellatif Moussaid Sanaa El Fkihi Yahya Zennayi Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
description	Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field.
format	article
author	Abdellatif Moussaid Sanaa El Fkihi Yahya Zennayi
author_facet	Abdellatif Moussaid Sanaa El Fkihi Yahya Zennayi
author_sort	Abdellatif Moussaid
title	Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
title_short	Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
title_full	Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
title_fullStr	Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
title_full_unstemmed	Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms
title_sort	tree crowns segmentation and classification in overlapping orchards based on satellite images and unsupervised learning algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/33ce09e75da54c9ca4d1a83fb016e350
work_keys_str_mv	AT abdellatifmoussaid treecrownssegmentationandclassificationinoverlappingorchardsbasedonsatelliteimagesandunsupervisedlearningalgorithms AT sanaaelfkihi treecrownssegmentationandclassificationinoverlappingorchardsbasedonsatelliteimagesandunsupervisedlearningalgorithms AT yahyazennayi treecrownssegmentationandclassificationinoverlappingorchardsbasedonsatelliteimagesandunsupervisedlearningalgorithms
_version_	1718411670624141312

Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

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