What question did this study set out to answer?

To develop an automated, transformer-based computer vision framework for detecting, counting, and assessing the health of date palm trees using large-scale UAV imagery.

March 30, 2024Remote Sensing Applications Society and EnvironmentOpen Access

Large-scale assessment of date palm plantations based on UAV remote sensing and multiscale vision transformer

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Authors

MGMohamed Barakat A. GibrilUniversity of Sharjah HSHelmi Zulhaidi Mohd ShafriUniversiti Putra Malaysia ASAbdallah ShanablehRISE Research Institutes of Sweden

Discussion

Member takes

Implication

Machine learning study demonstrates accurate detection and health monitoring of date palms via drone imagery, highlighting scalable precision agriculture tools.

Key Points

To develop an automated, transformer-based computer vision framework for detecting, counting, and assessing the health of date palm trees using large-scale UAV imagery.
Integrated a multiscale vision transformer with a feature pyramid network, Mask R–CNN, and slicing-aided hyper inference to process high-resolution drone imagery.
Employed transfer learning to classify palm health, distinguishing between healthy trees and those with severe pest infestations.
Compared model performance against multiple convolutional baseline architectures, including Mask R–CNN, Cascade Mask R–CNN, Point-based Rendering, and YOLACT.
Achieved F-scores of 94.33% for tree detection and 94.2% for instance segmentation, outperforming all benchmarked convolutional neural networks.
Identified overall tree health condition and severe pest infestations with an F-score of 88.4%.

Cite This Study

Gibril et al. (2024) studied this question.

synapsesocial.com/papers/6a72c04dac440176ef2ad4d5 https://doi.org/10.1016/j.rsase.2024.101195

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March 30, 2024Remote Sensing Applications Society and EnvironmentOpen Access

Large-scale assessment of date palm plantations based on UAV remote sensing and multiscale vision transformer

View Full Paper

Ask AI

Bookmark

Authors

MGMohamed Barakat A. GibrilUniversity of Sharjah HSHelmi Zulhaidi Mohd ShafriUniversiti Putra Malaysia ASAbdallah ShanablehRISE Research Institutes of Sweden

Discussion

Member takes

Implication

Machine learning study demonstrates accurate detection and health monitoring of date palms via drone imagery, highlighting scalable precision agriculture tools.

Key Points

To develop an automated, transformer-based computer vision framework for detecting, counting, and assessing the health of date palm trees using large-scale UAV imagery.
Integrated a multiscale vision transformer with a feature pyramid network, Mask R–CNN, and slicing-aided hyper inference to process high-resolution drone imagery.
Employed transfer learning to classify palm health, distinguishing between healthy trees and those with severe pest infestations.
Compared model performance against multiple convolutional baseline architectures, including Mask R–CNN, Cascade Mask R–CNN, Point-based Rendering, and YOLACT.
Achieved F-scores of 94.33% for tree detection and 94.2% for instance segmentation, outperforming all benchmarked convolutional neural networks.
Identified overall tree health condition and severe pest infestations with an F-score of 88.4%.

Cite This Study

Gibril et al. (2024) studied this question.

synapsesocial.com/papers/6a72c04dac440176ef2ad4d5 https://doi.org/10.1016/j.rsase.2024.101195

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