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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/7/11/241 |
| _version_ | 1797509842591547392 |
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| author | Abdellatif Moussaid Sanaa El Fkihi Yahya Zennayi |
| author_facet | Abdellatif Moussaid Sanaa El Fkihi Yahya Zennayi |
| author_sort | Abdellatif Moussaid |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:23:27Z |
| format | Article |
| id | doaj.art-33ce09e75da54c9ca4d1a83fb016e350 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T05:23:27Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-33ce09e75da54c9ca4d1a83fb016e3502023-11-22T23:52:27ZengMDPI AGJournal of Imaging2313-433X2021-11-0171124110.3390/jimaging7110241Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning AlgorithmsAbdellatif Moussaid0Sanaa El Fkihi1Yahya Zennayi2Information Retrieval and Data Analytics Laboratory, ENSIAS, Mohammed V University in Rabat, Rabat 10100, MoroccoInformation Retrieval and Data Analytics Laboratory, ENSIAS, Mohammed V University in Rabat, Rabat 10100, MoroccoEmbedded Systems and Artificial Intelligence Department, Moroccan Foundation for Advanced Science Innovation and Research (MAScIR), Rabat 10100, MoroccoSmart 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.https://www.mdpi.com/2313-433X/7/11/241tree canopy segmentationtree canopy classificationunsupervised learningsatellite imagesremote sensing |
| spellingShingle | Abdellatif Moussaid Sanaa El Fkihi Yahya Zennayi Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms Journal of Imaging tree canopy segmentation tree canopy classification unsupervised learning satellite images remote sensing |
| title | 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_short | 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 |
| topic | tree canopy segmentation tree canopy classification unsupervised learning satellite images remote sensing |
| url | https://www.mdpi.com/2313-433X/7/11/241 |
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