Extraction of the Individual Tree Infected by Pine Wilt Disease Using Unmanned Aerial Vehicle Optical Imagery

Zhou, Xiaojin; Liao, Lingmin; Cheng, Dan; Chen, X.; Huang, Qingsong

doi:10.5194/isprs-archives-xliii-b3-2020-247-2020

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Zhou et al used UAV-based RGB images to segment and detect individual trees infected by PWD. By using adaptive local threshold selection methods, infected trees in grayscale images could be automatically segmented according to the vegetation index (VEG) with an accuracy of 90% [22]. Deng et al set up a deep learning framework using faster region convolutional neural networks to detect PWD and the model accuracy reached 90% [23].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

2023

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Pine wilt disease (PWD), caused by pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a serious threat to the coniferous forests in China. This study used unmanned aerial vehicle (UAV)-based hyperspectral imaging conducted at different altitudes to investigate the impact of spatio-temporal scales on PWD detection in an monoculture Masson pine plantation. The influence of spatio-temporal scales on hyperspectral responses of pine trees infected with PWD and detection accuracies were evaluated by Jeffries–Matusita (J-M) distances and the random forest (RF) algorithm. The optimal vegetation indices (VIs) and spatial resolutions were identified by comparing feature importance and model accuracy. The main results showed that the VIs and J-M distances were greatly affected by spatio-temporal scales. In the early, mid-, and late infection stages, the RF-based PWD detection model had accuracies ranging between 72.05 and 79.48%, 83.71 and 89.59%, and 96.81 and 99.28%, peaking at the 10 cm, 8 cm, and 4 cm spatial resolutions, respectively. The green normalized difference vegetation index (GNDVI) and red edge position (REP) were the optimal VIs in early and mid-infection stages, respectively. This study can be important to improve the efficiency of PWD detection and reducing the loss of forests resources.

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Section: Introductionmentioning

confidence: 99%

Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

2023

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“…Pinewood nematodes often infringe on pine over large areas. Compared with manual field surveys, satellite and airborne remote sensing (including unmanned aerial vehicles [UAVs]) imaging provides faster acquisition of regional data to identify suspected pine trees infected with PWD (we use "infected pines" for short) [13][14][15]. As pinewood nematodes are too small to be identified in satellite remote sensing or UAV images, current methods based on these data for PWD identification often rely on the change of spectral reflectance of pines.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

et al. 2021

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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.

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The Effect of White Balance for Color Constancy on Visible Light

Kusnandar

Santoso

Surendro

2023

Proceedings of the 2023 12th International Conference on Software and Computer Applications

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Extraction of the Individual Tree Infected by Pine Wilt Disease Using Unmanned Aerial Vehicle Optical Imagery

Cited by 4 publications

References 12 publications

Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

The Effect of White Balance for Color Constancy on Visible Light

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