Nal neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral info, whereas the three-dimensional convolutional neural network (3D-CNN) is capable to gather this information from raw hyperspectral information. In this paper, we applied the residual block to FM4-64 Autophagy 3D-CNN and constructed a 3D-Res CNN model, the functionality of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11 and an accuracy of 72.86 for detecting early infected pine trees (EIPs). Employing only 20 of the instruction samples, the OA and EIP accuracy of 3D-Res CNN can nonetheless accomplish 81.06 and 51.97 , which is superior towards the state-of-the-art technique in the early detection of PWD based on hyperspectral pictures. Collectively, 3D-Res CNN was much more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD within this paper, creating the prediction and handle of PWD extra accurate and helpful. This model can also be applied to detect pine trees broken by other diseases or insect pests within the forest. Key phrases: pine wilt disease; early detection; UAV-based hyperspectral imagery; 3D-CNN; 3D-Res CNNPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.1. Introduction Pine wilt disease (PWD, also referred to as “cancer” of pine trees), brought on by the pine wood nematode (PWN; Bursaphelenchus xylophilus), is amongst the most harmful and prospective international quarantine forest ailments [1]. PWD originated in North America but now broadly occurs worldwide (Figure 1) [2], causing tremendous damages to the international forest ecosystems. Within a organic atmosphere, the pathogenic mechanism of PWD is as follows. When vector insects that carry the PWN emerge from the pine tree, they locate and feed around the bark of young shoots of pine tree branches, building wounds to the pine tree [6]. Then, the PWN invades the wound and eats the xylem with the pine tree [7,8], resulting in blockage of the tree’s vessel. Ultimately, the transpiration with the pine tree steadily loses its ML-SA1 Autophagy function,Copyright: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This short article is definitely an open access write-up distributed below the terms and conditions from the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ four.0/).Remote Sens. 2021, 13, 4065. https://doi.org/10.3390/rshttps://www.mdpi.com/journal/remotesensingRemote Sens. 2021, 13, x FOR PEER Review in blockageRemote Sens. 2021, 13,[6]. Then, the PWN invades the wound and eats the xylem of the pine tree [7,8], resulting 2 of 23 on the tree’s vessel. Lastly, the transpiration in the pine tree progressively loses its function, and the water absorbed by the root cannot attain the crown; therefore, the pine tree needles wither, and ultimately the entire pine tree dies. The detailed procedure of PWN 2 of 22 infection the PWN invades two. [6]. Then, is shown in Figurethe wound and eats the xylem of your pine tree [7,8], resulting in blockage from the tree’s vessel. Lastly, the transpiration in the pine tree gradually loses its function, along with the water absorbed by the root cannot reach the crown; hence, the pine and the water absorbed by the root can’t reach the dies. The detailed procedure needles tree needles wither, and sooner or later the whole pine t.
