Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture
Object detection in agriculture is vital for identifying and mapping agricultural areas, especially with the growth of precision farming technologies. Traditional methods for counting and yield estimation are time-consuming and demand significant physical effort, presenting substantial challenges fo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003582 |
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| author | Youness Hnida Mohamed Adnane Mahraz Ali Yahyaouy Ali Achebour Jamal Riffi Hamid Tairi |
| author_facet | Youness Hnida Mohamed Adnane Mahraz Ali Yahyaouy Ali Achebour Jamal Riffi Hamid Tairi |
| author_sort | Youness Hnida |
| collection | DOAJ |
| description | Object detection in agriculture is vital for identifying and mapping agricultural areas, especially with the growth of precision farming technologies. Traditional methods for counting and yield estimation are time-consuming and demand significant physical effort, presenting substantial challenges for farmers. The use of drones and artificial intelligence, particularly deep learning, has transformed agricultural monitoring, enabling more accurate and rapid analyses. In this study, we introduce an advanced method for detecting tree crowns, focusing on olive trees in farm environments. Our approach is based on an innovative architecture that incorporates a Cross Stage Partial Network (CSPNet) combined with a Feature Pyramid Network (FPN) and Path Aggregation Network (PAN), augmented by DropBlock regularization. Our architecture is tailored for multi-scale object detection from UAV-captured imagery, addressing issues such as small object detection, complex backgrounds, object rotation, scale variations, and category imbalances in both simple imagery and high-resolution orthophotos. These orthophotos are produced by stitching together multiple high-quality images we captured from various angles and altitudes to create a comprehensive and detailed view of the orchard. Our methodology includes splitting images into different sizes (1 × 1, 3 × 3, 6 × 6, and 9 × 9) to enhance analysis and improve detection performance at various scales. This comprehensive approach has enabled us to conduct an in-depth analysis of olive trees, classified into small, medium, and large sizes. The results demonstrate the robustness of our method in addressing common object detection challenges in agricultural contexts, achieving a precision of 92.47 %, recall of 91.40 %, F1-score of 91.93 %, mAP@0.5 of 94.00 %, and mAP@[0.5:0.95] of 87.00 %. These results confirm its reliability for optimizing precision farming practices, including crop condition monitoring and resource management. |
| format | Article |
| id | doaj-art-312e32f9847d4d3089f4adb080b32518 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-312e32f9847d4d3089f4adb080b325182025-08-20T02:36:19ZengElsevierSmart Agricultural Technology2772-37552025-12-011210112610.1016/j.atech.2025.101126Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architectureYouness Hnida0Mohamed Adnane Mahraz1Ali Yahyaouy2Ali Achebour3Jamal Riffi4Hamid Tairi5L3IA - Laboratory of Computer Science, Innovation, and Artificial Intelligence, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco; Research and Development Department, Drone Globe, Rabat, Morocco; Corresponding author.L3IA - Laboratory of Computer Science, Innovation, and Artificial Intelligence, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, MoroccoL3IA - Laboratory of Computer Science, Innovation, and Artificial Intelligence, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco; LaMSN - La Maison des Sciences Numériques, USPN, Paris, FranceGeographic Information Technology and Space Management Team (ETIGGE), Laboratory of Communication, Education, Digital Uses, and Creativity, Faculty of Arts and Humanities, Mohamed 1st University, Oujda, Morocco; Research and Development Department, Drone Globe, Rabat, MoroccoL3IA - Laboratory of Computer Science, Innovation, and Artificial Intelligence, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, MoroccoL3IA - Laboratory of Computer Science, Innovation, and Artificial Intelligence, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, MoroccoObject detection in agriculture is vital for identifying and mapping agricultural areas, especially with the growth of precision farming technologies. Traditional methods for counting and yield estimation are time-consuming and demand significant physical effort, presenting substantial challenges for farmers. The use of drones and artificial intelligence, particularly deep learning, has transformed agricultural monitoring, enabling more accurate and rapid analyses. In this study, we introduce an advanced method for detecting tree crowns, focusing on olive trees in farm environments. Our approach is based on an innovative architecture that incorporates a Cross Stage Partial Network (CSPNet) combined with a Feature Pyramid Network (FPN) and Path Aggregation Network (PAN), augmented by DropBlock regularization. Our architecture is tailored for multi-scale object detection from UAV-captured imagery, addressing issues such as small object detection, complex backgrounds, object rotation, scale variations, and category imbalances in both simple imagery and high-resolution orthophotos. These orthophotos are produced by stitching together multiple high-quality images we captured from various angles and altitudes to create a comprehensive and detailed view of the orchard. Our methodology includes splitting images into different sizes (1 × 1, 3 × 3, 6 × 6, and 9 × 9) to enhance analysis and improve detection performance at various scales. This comprehensive approach has enabled us to conduct an in-depth analysis of olive trees, classified into small, medium, and large sizes. The results demonstrate the robustness of our method in addressing common object detection challenges in agricultural contexts, achieving a precision of 92.47 %, recall of 91.40 %, F1-score of 91.93 %, mAP@0.5 of 94.00 %, and mAP@[0.5:0.95] of 87.00 %. These results confirm its reliability for optimizing precision farming practices, including crop condition monitoring and resource management.http://www.sciencedirect.com/science/article/pii/S2772375525003582Precision farmingMulti-scale detectionUAV imageryOrthophotosDeep learning |
| spellingShingle | Youness Hnida Mohamed Adnane Mahraz Ali Yahyaouy Ali Achebour Jamal Riffi Hamid Tairi Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture Smart Agricultural Technology Precision farming Multi-scale detection UAV imagery Orthophotos Deep learning |
| title | Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture |
| title_full | Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture |
| title_fullStr | Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture |
| title_full_unstemmed | Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture |
| title_short | Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture |
| title_sort | enhanced multi scale detection of olive tree crowns in uav orthophotos using a deep learning architecture |
| topic | Precision farming Multi-scale detection UAV imagery Orthophotos Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003582 |
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