Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

Olfactory proteins form the basis of insect olfactory recognition, which is crucial for host identification, mating, and oviposition. Using transcriptome analysis of Anoplophora glabripennis antenna, we identified 42 odorant-binding proteins (OBPs), 12 chemosensory proteins (CSPs), 14 pheromone-degrading enzymes (PDEs), 1 odorant-degrading enzymes (ODE), 37 odorant receptors (ORs), 11 gustatory receptors (GRs), 2 sensory neuron membrane proteins (SNMPs), and 4 ionotropic receptor (IR). All CSPs and PBPs were expressed in antennae, confirming the authenticity of the transcriptome data. CSP expression profiles showed that AglaCSP3, AglaCSP6, and AglaCSP12 were expressed preferentially in maxillary palps and AglaCSP7 and AglaCSP9 were strongly expressed in antennae. The vast majority of CSPs were highly expressed in multiple chemosensory tissues, suggesting their participation in olfactory recognition in almost all olfactory tissues. Intriguingly, the PBP AglaPBP2 was preferentially expressed in antenna, indicating that it is the main protein involved in efficient and sensitive pheromone recognition. Phylogenetic analysis of olfactory proteins indicated AglaGR1 may detect CO₂. This study establishes a foundation for determining the chemoreception molecular mechanisms of A. glabripennis, which would provide a new perspective for controlling pest populations, especially those of borers.

Pine wilt disease (PWD) caused by the pine wood nematode (PWN) is China’s most serious forest disease in recent decades. Its complex disease system with unclear pathogenesis and differentiated pathogenicity makes it challenging to manage and control. PWD has spread rapidly in Northeast and Northwest China in recent years, exceeding previous predictions and the traditional understanding of its potential distribution. This paper reviewed the comprehensive risk of PWD in Northeast and Northwest China through the occurrence of influencing factors. Regarding PWN’s temperature resistance, all regions of China are suitable for PWD. In terms of altitude, the PWD system is mainly distributed below 1000 m. In China, Pinus and Larix species are confirmed host trees of PWN, while Monochamus alternatus and M. saltuarius are confirmed vector insects of PWN. In this case, PWD may occur in all low-altitude pine forests in China. Northeast China has a higher risk than Northwest China due to its distribution of suitable host trees and vector beetles at low altitudes. To confront the threat, two scientific bottlenecks are required to be demonstrated. The first is the pathogenicity of Chinese PWN strains to Chinese Picea and Abies species. The other is the geographical distribution and highest altitude distribution of other Monochamus species in the Palaearctic region of China and their PWN transmission ability. We also suggest strengthening plant quarantining in Northeast China. In conclusion, this review comprehensively analyzes the risk of PWD in Northeast and Northwest China in terms of temperature, altitude, host trees, and vector insects. It provides a new perspective and reference for the management and research of PWD in China.

Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted.

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance 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). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.