Camellia oleifera Tree Detection and Counting Based on UAV RGB Image and YOLOv8

Renxu Yang, Debao Yuan (), Maochen Zhao, Zhao Zhao, Liuya Zhang, Yuqing Fan, Guangyu Liang and Yifei Zhou
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Renxu Yang: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Debao Yuan: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Maochen Zhao: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Zhao Zhao: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Liuya Zhang: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Yuqing Fan: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Guangyu Liang: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Yifei Zhou: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 10, 1-15

Abstract: The detection and counting of Camellia oleifera trees are important parts of the yield estimation of Camellia oleifera . The ability to identify and count Camellia oleifera trees quickly has always been important in the context of research on the yield estimation of Camellia oleifera . Because of their specific growing environment, it is a difficult task to identify and count Camellia oleifera trees with high efficiency. In this paper, based on a UAV RGB image, three different types of datasets, i.e., a DOM dataset, an original image dataset, and a cropped original image dataset, were designed. Combined with the YOLOv8 model, the detection and counting of Camellia oleifera trees were carried out. By comparing YOLOv9 and YOLOv10 in four evaluation indexes, including precision, recall, mAP, and F1 score, Camellia oleifera trees in two areas were selected for prediction and compared with the real values. The experimental results show that the cropped original image dataset was better for the recognition and counting of Camellia oleifera , and the mAP values were 8% and 11% higher than those of the DOM dataset and the original image dataset, respectively. Compared to YOLOv5, YOLOv7, YOLOv9, and YOLOv10, YOLOv8 performed better in terms of the accuracy and recall rate, and the mAP improved by 3–8%, reaching 0.82. Regression analysis was performed on the predicted and measured values, and the average R 2 reached 0.94. This research shows that a UAV RGB image combined with YOLOv8 provides an effective solution for the detection and counting of Camellia oleifera trees, which is of great significance for Camellia oleifera yield estimation and orchard management.

Keywords: Camellia oleifera trees; tree counting; UAV RGB image; deep learning; YOLOv8 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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