Identifying discolored trees inflected with pine wilt disease using DSSN-based UAV remote sensing
ZHANG Ruirui1,2,3(), XIA Lang1,2,3, CHEN Liping1,2,3(), DING Chenchen1,2,3, ZHENG Aichun4, HU Xinmiao4, YI Tongchuan1,2,3, CHEN Meixiang1,2,3, CHEN Tianen1,2,3,5
1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China 2. National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China 4. Nanjing Pukou District Forestry Station, Nanjing 211899, China 5. Nongxin (Nanjing) Intelligent Agricultural Research Institute Co., Ltd., Nanjing 211899, China
Pine wilt disease (PWD) is identified as a major disease endangering the forest resources in China. Investigating the deep semantic segmentation network (DSSN)-based unmanned aerial vehicle (UAV) remote sensing identification can improve the identification accuracy of discolored trees infected with PWD and provide technical support for the enhancement and protection of the forest resource quality. Focusing on the pine forest in Laoshan Mountain in Qingdao, this study obtained images of suspected discolored trees through aerial photography using a fixed-wing UAV. To examine four deep semantic segmentation models, namely fully convolutional network (FCN), U-Net, DeepLabV3+, and object context network (OCNet), this study assessed the segmentation accuracies of the four models using recall, precision, IoU, and F1 score. Based on the 2 688 images acquired, 28 800 training samples were obtained through manual labeling and sample amplification. The results indicate that the four models can effectively identify the discolored trees infected with PWD, with no significant false alarms. Furthermore, these deep learning models efficiently distinguished between surface features with similar colors, such as rocks and yellow bare soils. Generally, DeeplabV3+ outperformed the remaining three models, with an IoU of 0.711 and an F1 score of 0.711. In contrast, the FCN model exhibited the lowest segmentation accuracy, with an IoU of 0.699 and an F1 score of 0.812. DeeplabV3+ proved the least time-consuming time for training, requiring merely 27.2 ms per image. Meanwhile, FCN was the least time-consuming in prediction, with only 7.2 ms needed per image. However, this model exhibited the lowest edge segmentation accuracy of discolored trees. Three DeepLabV3+ models constructed using Resnet50, Resnet101, and Resnet152 as front-end feature extraction networks exhibited IoU of 0.711, 0.702, and 0.702 and F1 scores of 0.829, 0.822, and 0.820, respectively. DeepLabV3+ surpassed DeepLabV3 in the identification accuracy of discolored trees, with the letter showing an IoU of 0.701 and an F1 score of 0.812. The train data revealed that DeepLabV3+ exhibited the highest identification accuracy of the discolored trees, while the ResNet feature extraction network produced minor impacts on the identification accuracy. The encoding and decoding structures introduced by DeepLabV3+ can significantly improve the segmentation accuracy of DeepLabV3, yielding more detailed edges. Therefore, DeepLabV3+ is more favorable for the identification of discolored trees infected with PWD.
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2024, Vol. 36 
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