Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network
Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB image...
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MDPI AG
2024-10-01
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| author | Min-Seok Gang Thanyachanok Sutthanonkul Won Suk Lee Shiyu Liu Hak-Jin Kim |
| author_facet | Min-Seok Gang Thanyachanok Sutthanonkul Won Suk Lee Shiyu Liu Hak-Jin Kim |
| author_sort | Min-Seok Gang |
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| description | Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R<sup>2</sup>), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm<sup>3</sup>, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant’s canopy volumes, which is crucial for implementing site-specific management of strawberry crops. |
| format | Article |
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| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-bf34aed5097743d6990bfad3e99bfbcb2025-08-20T02:13:19ZengMDPI AGSensors1424-82202024-10-012421692010.3390/s24216920Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural NetworkMin-Seok Gang0Thanyachanok Sutthanonkul1Won Suk Lee2Shiyu Liu3Hak-Jin Kim4Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of KoreaDepartment of Agricultural & Biological Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Agricultural & Biological Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Agricultural & Biological Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of KoreaEstimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R<sup>2</sup>), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm<sup>3</sup>, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant’s canopy volumes, which is crucial for implementing site-specific management of strawberry crops.https://www.mdpi.com/1424-8220/24/21/6920canopy volumegrowth estimationUAVYOLORGB images |
| spellingShingle | Min-Seok Gang Thanyachanok Sutthanonkul Won Suk Lee Shiyu Liu Hak-Jin Kim Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network Sensors canopy volume growth estimation UAV YOLO RGB images |
| title | Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network |
| title_full | Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network |
| title_fullStr | Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network |
| title_full_unstemmed | Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network |
| title_short | Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network |
| title_sort | estimation of strawberry canopy volume in unmanned aerial vehicle rgb imagery using an object detection based convolutional neural network |
| topic | canopy volume growth estimation UAV YOLO RGB images |
| url | https://www.mdpi.com/1424-8220/24/21/6920 |
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