A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model
Quantifying planting layouts during the seedling stage of mung beans (<i>Vigna radiata</i> L.) is crucial for assessing cultivation conditions and providing support for precise management. Traditional information extraction methods are often hindered by engineering workloads, time consum...
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/151 |
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| author | Kun Yang Xiaohua Sun Ruofan Li Zhenxue He Xinxin Wang Chao Wang Bin Wang Fushun Wang Hongquan Liu |
| author_facet | Kun Yang Xiaohua Sun Ruofan Li Zhenxue He Xinxin Wang Chao Wang Bin Wang Fushun Wang Hongquan Liu |
| author_sort | Kun Yang |
| collection | DOAJ |
| description | Quantifying planting layouts during the seedling stage of mung beans (<i>Vigna radiata</i> L.) is crucial for assessing cultivation conditions and providing support for precise management. Traditional information extraction methods are often hindered by engineering workloads, time consumption, and labor costs. Applying deep-learning technologies for information extraction reduces these burdens and yields precise and reliable results, enabling a visual analysis of seedling distribution. In this work, an unmanned aerial vehicle (UAV) was employed to capture visible light images of mung bean seedlings in a field across three height gradients of 2 m, 5 m, and 7 m following a time series approach. To improve detection accuracy, a small target detection layer (p2) was integrated into the YOLOv8-obb model, facilitating the identification of mung bean seedlings. Image detection performance and seedling information were analyzed considering various dates, heights, and resolutions, and the K-means algorithm was utilized to cluster feature points and extract row information. Linear fitting was performed via the least squares method to calculate planting layout parameters. The results indicated that on the 13th day post seeding, a 2640 × 1978 image captured at 7 m above ground level exhibited optimal detection performance. Compared with YOLOv8, YOLOv8-obb, YOLOv9, and YOLOv10, the YOLOv8-obb-p2 model improved precision by 1.6%, 0.1%, 0.3%, and 2%, respectively, and F1 scores improved by 2.8%, 0.5%, 0.5%, and 3%, respectively. This model extracts precise information, providing reliable data for quantifying planting layout parameters. These findings can be utilized for rapid and large-scale assessments of mung bean seedling growth and development, providing theoretical and technical support for seedling counting and planting layouts in hole-seeded crops. |
| format | Article |
| id | doaj-art-5e88c16e1e414f908e88d8d45cf46793 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-5e88c16e1e414f908e88d8d45cf467932025-01-24T13:16:56ZengMDPI AGAgronomy2073-43952025-01-0115115110.3390/agronomy15010151A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb ModelKun Yang0Xiaohua Sun1Ruofan Li2Zhenxue He3Xinxin Wang4Chao Wang5Bin Wang6Fushun Wang7Hongquan Liu8College of Information Science and Technology, Hebei Agricultural University, Baoding 071000, ChinaDepartment of Digital Media, Hebei Software Institute, Baoding 071000, ChinaCollege of Information Science and Technology, Hebei Agricultural University, Baoding 071000, ChinaCollege of Information Science and Technology, Hebei Agricultural University, Baoding 071000, ChinaCollege of Horticulture, Hebei Agricultural University, Baoding 071000, ChinaCollege of Information Science and Technology, Hebei Agricultural University, Baoding 071000, ChinaCollege of Information Science and Technology, Hebei Agricultural University, Baoding 071000, ChinaCollege of Information Science and Technology, Hebei Agricultural University, Baoding 071000, ChinaCollege of Urban and Rural Construction, Hebei Agricultural University, Baoding 071000, ChinaQuantifying planting layouts during the seedling stage of mung beans (<i>Vigna radiata</i> L.) is crucial for assessing cultivation conditions and providing support for precise management. Traditional information extraction methods are often hindered by engineering workloads, time consumption, and labor costs. Applying deep-learning technologies for information extraction reduces these burdens and yields precise and reliable results, enabling a visual analysis of seedling distribution. In this work, an unmanned aerial vehicle (UAV) was employed to capture visible light images of mung bean seedlings in a field across three height gradients of 2 m, 5 m, and 7 m following a time series approach. To improve detection accuracy, a small target detection layer (p2) was integrated into the YOLOv8-obb model, facilitating the identification of mung bean seedlings. Image detection performance and seedling information were analyzed considering various dates, heights, and resolutions, and the K-means algorithm was utilized to cluster feature points and extract row information. Linear fitting was performed via the least squares method to calculate planting layout parameters. The results indicated that on the 13th day post seeding, a 2640 × 1978 image captured at 7 m above ground level exhibited optimal detection performance. Compared with YOLOv8, YOLOv8-obb, YOLOv9, and YOLOv10, the YOLOv8-obb-p2 model improved precision by 1.6%, 0.1%, 0.3%, and 2%, respectively, and F1 scores improved by 2.8%, 0.5%, 0.5%, and 3%, respectively. This model extracts precise information, providing reliable data for quantifying planting layout parameters. These findings can be utilized for rapid and large-scale assessments of mung bean seedling growth and development, providing theoretical and technical support for seedling counting and planting layouts in hole-seeded crops.https://www.mdpi.com/2073-4395/15/1/151UAV imageYOLOv8-obbobject detectionmung beanplanting layoutlinear fitting |
| spellingShingle | Kun Yang Xiaohua Sun Ruofan Li Zhenxue He Xinxin Wang Chao Wang Bin Wang Fushun Wang Hongquan Liu A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model Agronomy UAV image YOLOv8-obb object detection mung bean planting layout linear fitting |
| title | A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model |
| title_full | A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model |
| title_fullStr | A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model |
| title_full_unstemmed | A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model |
| title_short | A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model |
| title_sort | method for quantifying mung bean field planting layouts using uav images and an improved yolov8 obb model |
| topic | UAV image YOLOv8-obb object detection mung bean planting layout linear fitting |
| url | https://www.mdpi.com/2073-4395/15/1/151 |
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