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Pine wilt disease (PWD) is a severe forest disease caused by the invasion of pine wood nematode (Bursaphelenchus xylophilus), which has caused significant damage to China’s forestry resources due to its short disease cycle and strong infectious ability. Benefiting from the development of unmanned aerial vehicle (UAV)-based remote sensing technology, the use of UAV images for the detection of PWD-infected trees has become one of the mainstream methods. However, current UAV-based detection studies mostly focus on multispectral and hyperspectral images, and few studies have focused on using red–green–blue (RGB) images for detection. This study used UAV-based RGB images to extract feature information using different color space models and then utilized semantic segmentation techniques in deep learning to detect individual PWD-infected trees. The results showed that: (1) The U-Net model realized the optimal image segmentation and achieved the highest classification accuracy with F1-score, recall, and Intersection over Union (IoU) of 0.9586, 0.9553, and 0.9221, followed by the DeepLabv3+ model and the feature pyramid networks (FPN) model. (2) The RGBHSV feature scheme outperformed both the RGB feature scheme and the hue saturation value (HSV) feature scheme, which were unrelated to the choice of the semantic segmentation techniques. (3) The semantic segmentation techniques in deep-learning models achieved superior model performance compared with traditional machine-learning methods, with the U-Net model obtaining 4.81% higher classification accuracy compared with the random forest model. (4) Compared to traditional semantic segmentation models, the newly proposed segment anything model (SAM) performed poorly in identifying pine wood nematode disease. Its success rate is 0.1533 lower than that of the U-Net model when using the RGB feature scheme and 0.2373 lower when using the HSV feature scheme. The results showed that the U-Net model using the RGBHSV feature scheme performed best in detecting individual PWD-infected trees, indicating that the proposed method using semantic segmentation technique and UAV-based RGB images to detect individual PWD-infected trees is feasible. The proposed method not only provides a cost-effective solution for timely monitoring forest health but also provides a precise means to conduct remote sensing image classification tasks. Full article

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models. Full article