Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models
Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/10/1633 |
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| author | Daniel G. García-Murillo J. Caicedo-Acosta G. Castellanos-Dominguez |
| author_facet | Daniel G. García-Murillo J. Caicedo-Acosta G. Castellanos-Dominguez |
| author_sort | Daniel G. García-Murillo |
| collection | DOAJ |
| description | Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do. |
| first_indexed | 2024-03-10T19:43:22Z |
| format | Article |
| id | doaj.art-ff1b5bb06fda4466a6d020f908d32ebc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:43:22Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ff1b5bb06fda4466a6d020f908d32ebc2023-11-20T01:04:52ZengMDPI AGRemote Sensing2072-42922020-05-011210163310.3390/rs12101633Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface ModelsDaniel G. García-Murillo0J. Caicedo-Acosta1G. Castellanos-Dominguez2Signal Processing and Recognition Group, Universidad Nacional de Colombia, sede Manizales, MNZ 170003, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, sede Manizales, MNZ 170003, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, sede Manizales, MNZ 170003, ColombiaIndividual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do.https://www.mdpi.com/2072-4292/12/10/1633digital surface modelsindividual tree detectionextended maxima transformfarm monitoring |
| spellingShingle | Daniel G. García-Murillo J. Caicedo-Acosta G. Castellanos-Dominguez Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models Remote Sensing digital surface models individual tree detection extended maxima transform farm monitoring |
| title | Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models |
| title_full | Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models |
| title_fullStr | Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models |
| title_full_unstemmed | Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models |
| title_short | Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models |
| title_sort | individual detection of citrus and avocado trees using extended maxima transform summation on digital surface models |
| topic | digital surface models individual tree detection extended maxima transform farm monitoring |
| url | https://www.mdpi.com/2072-4292/12/10/1633 |
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