Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model

Yu Ji; Enping Yan; Xianming Yin; Yabin Song; Wei Wei; Dengkui Mo

doi:10.3389/fpls.2022.958940

Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model

Front Plant Sci. 2022 Aug 11:13:958940. doi: 10.3389/fpls.2022.958940. eCollection 2022.

Authors

Yu Ji^{1

2

3}, Enping Yan^{1

2

3}, Xianming Yin^{1

2

3}, Yabin Song⁴, Wei Wei⁵, Dengkui Mo^{1

2

3}

Affiliations

¹ Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, China.
² Key Laboratory of State Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, China.
³ College of Forestry, Central South University of Forestry and Technology, Changsha, China.
⁴ Central South Forest Inventory and Planning Institute of State Forestry Administration, Changsha, China.
⁵ Forestry Research Institute of Guangxi Zhuang Autonomous Region, Guangxi, China.

Abstract

As one of the four most important woody oil-tree in the world, Camellia oleifera has significant economic value. Rapid and accurate acquisition of C. oleifera tree-crown information is essential for enhancing the effectiveness of C. oleifera tree management and accurately predicting fruit yield. This study is the first of its kind to explore training the ResU-Net model with UAV (unmanned aerial vehicle) images containing elevation information for automatically detecting tree crowns and estimating crown width (CW) and crown projection area (CPA) to rapidly extract tree-crown information. A Phantom 4 RTK UAV was utilized to acquire high-resolution images of the research site. Using UAV imagery, the tree crown was manually delineated. ResU-Net model's training dataset was compiled using six distinct band combinations of UAV imagery containing elevation information [RGB (red, green, and blue), RGB-CHM (canopy height model), RGB-DSM (digital surface model), EXG (excess green index), EXG-CHM, and EXG-DSM]. As a test set, images with UAV-based CW and CPA reference values were used to assess model performance. With the RGB-CHM combination, ResU-Net achieved superior performance. Individual tree-crown detection was remarkably accurate (Precision = 88.73%, Recall = 80.43%, and F1score = 84.68%). The estimated CW (R ² = 0.9271, RMSE = 0.1282 m, rRMSE = 6.47%) and CPA (R ² = 0.9498, RMSE = 0.2675 m², rRMSE = 9.39%) values were highly correlated with the UAV-based reference values. The results demonstrate that the input image containing a CHM achieves more accurate crown delineation than an image containing a DSM. The accuracy and efficacy of ResU-Net in extracting C. oleifera tree-crown information have great potential for application in non-wood forests precision management.

Keywords: Camellia oleifera; UAV imagery; deep learning; image segmentation; tree-crown detection.