Pine wilt disease (PWD) has become increasingly serious recently and causes great damage to the world’s pine forest resources. The use of unmanned aerial vehicle (UAV)-based remote sensing helps to identify pine nematode trees in time and has become a feasible and effective approach to precisely monitor PWD infection. However, a rapid and high-accuracy detection approach has not been well established in a complex terrain environment. To this end, a deep learning-based pine nematode tree identification method is proposed by fusing visible and multispectral imagery. A UAV equipped with a multispectral camera and a visible camera was used to obtain imagery, where multispectral imagery includes six bands, i.e., red, green, blue, near-infrared, red edge and red edge 750 nm. Two vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) are extracted as a typical feature according to the reflectance of infected trees in different spectral bands. The YOLOv5 (You Only Look Once v5)-based detection algorithm is adopted and optimized from different aspects to realize the identification of infected pine trees with high detection speed and accuracy. e.g., GhostNet is adopted to reduce the number of model parameters and improve the detection speed; a module combining a CBAM (Convolutional Block Attention Module) and a CA (Coordinate Attention) mechanism is designed to improve the feature extraction for small-scale pine nematode trees; Transformer module and BiFPN (Bidirectional Feature Pyramid Network) structure are applied to improve the feature fusion capability. The experiments show that the mAP@0.5 of the improved YOLOv5 model is 98.7%, the precision is 98.1%, the recall is 97.3%, the average detection speed of single imagery is 0.067 s, and the model size is 46.69 MB. All these metrics outperform other comparison methods. Therefore, the proposed method can achieve a fast and accurate detection of pine nematode trees, providing effective technical support for the control of a pine nematode epidemic.

Cuticular hydrocarbons of Cerambycidae species can function as signals for sex recognition. Little is known about the copulatory signals of the juniper bark borer Semanotus bifasciatus, a major economic threat to Platycladus orientalis Franco in China. Here, we investigated the cuticular hydrocarbons of both sexes of S. bifasciatus to determine the chemically mediated mating signals using the solid-phase microextraction (SPME) technique with carbowax/divinylbenzene fibers (CAR/DVB) and then analyzed by coupled gas chromatographymass spectrometry (GC-MS). A series of aliphatic saturated straight-chain n-alkanes (n-C 23 to n-C 28 ), internally branched monomethylalkanes at carbons 3, 11, or 13, and dimethylalkanes were identified, which showed no qualitative differences in either sex and were similar in the samples with SPME fiber extraction and those with hexane extraction. The bioassay showed that 11-methylpentacosane (11-MeC 25 ), 11-methylhexacosane (11-MeC 26 ), and 11-methylheptacosane (11-MeC 27 ) have sex-specific recognition functions that triggered more mating attempts at a female-specific ratio of 100:4:60 than at a male-specific ratio of 100:85:50. In addition, the female-specific ratio of 11-methylalkanes can elicit about 70% of male mating attempts within about 60 s, whereas live females elicit about 98% of male mating attempts within 25 s. The discrepancy in the initiation of mating attempts by synthetic mixtures and live females suggests that the methyl isomers 3-MeC 25 , 3-MeC 27 , and/or 11,15-diMeC 27 may also be involved in the mating behavior of S. bifasciatus. These results suggest that 11-MeC 25 , 11-MeC 26 , and 11-MeC 27 constitute the contact sex pheromone of S. bifasciatus, with the presence or absence of 11-MeC 26 in particular playing an important role in mate recognition by males.

Pine wilt disease is a major biological disaster caused by Bursaphelenchus xylophilus. This study establishes a high-quality, and well-annotated genome sequence of B. xylophilus strain TS-1 from Mountain Tai of Shandong province, China. The 75-Mbp assembly containing six chromosomes was established. This genome data represents a new valuable resource for future studies on B. xylophilus and the management of pine wilt disease.