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Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network’s feature extraction capabilities and increase the model’s sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

Pest and disease damage to forests cannot be underestimated, so it is essential to detect diseased trees in time and take measures to stop their spread. The detection of discoloration standing trees is one of the important means to effectively control the spread of pests and diseases. In the visible wavelength range, early infected trees do not show significant color changes, which poses a challenge for early detection and is only suitable for monitoring middle and late discolor trees. The spectral resolution of hyperspectral restricts the improvement of its spatial resolution, and there are phenomena of different spectral of the same and foreign objects in the same spectrum, which affect the detection results. In this paper, the method of hyperspectral and CCD image fusion is used to achieve high-precision detection of discoloration standing trees. This paper proposes an improved algorithm MSGF-GLP, which uses multi-scale detail boosting and MTF filter to refine high-resolution data. By combining guided filtering with hyperspectral images, the spatial detail difference is enhanced, and the injection gain is interpolated into the difference of each band, so as to obtain high-resolution and high-quality hyperspectral images. This research is based on hyperspectral and CCD data obtained from LiCHy, Chinese Academy of Forestry, Maoershan Experimental Forest Farm, Shangzhi City, Heilongjiang Province. The evaluation framework is used to compare with the other five fusion algorithms to verify the good effect of the proposed method, which can effectively preserve the canopy spectrum and improve the spatial details. The fusion results of forestry remote sensing data were analyzed using the vegetation Normalized Difference Water Index and Plant Senescence Reflectance Index. The fused results can be used to distinguish the difference between discoloration trees and healthy trees by the multispectral vegetation index. The research results can provide good technical support for the practical application of forest remote sensing data fusion, and lay the foundation for promoting the scientific, automatic and intelligent forestry control.

Typical thioredoxin (TRX) plays an important role in maintaining redox balance in plants. However, the typical TRX genes in wheat still need to be comprehensively and deeply studied. In this research, a total of 48 typical TaTRX genes belonging to eight subtypes were identified via a genome-wide search in wheat, and the gene structures, protein conserved motifs, and protein 3D structures of the same subtype were very similar. Evolutionary analysis showed that there are two pairs of tandem duplication genes and 14 clusters of segmental duplication genes in typical TaTRX family members; TaTRX15, TaTRX36, and TaTRX42 had positive selection compared with the orthologs of their ancestral species; rice and maize have 11 and 13 orthologous typical TRXs with wheat, respectively. Gene Ontology (GO) analysis indicated that typical TaTRXs were involved in maintaining redox homeostasis in wheat cells. Estimation of ROS content, determination of antioxidant enzyme activity, and gene expression analysis in a line overexpressing one typical TaTRX confirmed that TRX plays an important role in maintaining redox balance in wheat. A predictive analysis of cis-acting elements in the promoter region showed that typical TaTRXs were extensively involved in various hormone metabolism and response processes to stress. The results predicted using public databases or verified using RT-qPCR show that typical TaTRXs were able to respond to biotic and abiotic stresses, and their expression in wheat was spatiotemporal. A total of 16 wheat proteins belonging to four different families interacting with typical TaTRXs were predicted. The above comprehensive analysis of typical TaTRX genes can enrich our understanding of this gene family in wheat and provide valuable insights for further gene function research.

Thioredoxins (TRXs) are small-molecule proteins with redox activity that play very important roles in the growth, development, and stress resistance of plants. Foxtail millet (Setaria italica) gradually became a model crop for stress resistance research because of its advantages such as its resistance to sterility and its small genome. To date, the thioredoxin (TRX) family has been identified in Arabidopsis thaliana, rice and wheat. However, studies of the TRX family in foxtail millet have not been reported, and the biological function of this family remains unclear. In this study, 35 SiTRX genes were identified in the whole genome of foxtail millet through bioinformatic analysis. According to phylogenetic analysis, 35 SiTRXs can be divided into 13 types. The chromosome distribution, gene structure, cis-elements and conserved protein motifs of 35 SiTRXs were characterized. Three nucleoredoxin (NRX) members were further identified by a structural analysis of TRX family members. The expression patterns of foxtail millet’s SiNRX members under abiotic stresses showed that they have different stress-response patterns. In addition, subcellular localization revealed that SiNRXs were localized to the nucleus, cytoplasm and membrane. Further studies demonstrated that the overexpression of SiNRX1 enhanced Arabidopsis’ tolerance to drought and salt stresses, resulting in a higher survival rate and better growth performance. Moreover, the expression levels of several known stress-related genes were generally higher in overexpressed lines than in the wild-type. Thus, this study provides a general picture of the TRX family in foxtail millet and lay a foundation for further research on the mechanism of the action of TRX proteins on abiotic stresses.

Drought and salt stress can strongly affect the growth and development of wheat. Wheat adapts to drought and salt stress through osmotic regulation. Betaine aldehyde dehydrogenase (BADH) is a key enzyme in the synthesis of betaine, an osmotic regulator. We cloned a region of the TaBADH-A1 promoter and genomic DNA that included the introns and exons, from four Chinese wheat cultivars. Following the analysis of TaBADH-A1 genomic DNA and promoter sequence polymorphisms of 4 cloned and 15 cultivars from the database, 7 haplotypes of TaBADH-A1 gene were identified. We divided the 7 haplotypes with a 254 bp insertion or deletion (indel) into two main alleles, BADH-A1a and BADH-A1b. Meanwhile, a molecular marker was developed based on the 254 bp indel of the third intron of TaBADH-A1 gene. Expression levels of BADH-A1b were found to be significantly higher than those of BADH-A1a under drought and salt stress conditions. Betaine accumulation was significantly higher in wheat containing BADH-A1b compared to BADH-A1a under drought and salt stress. We also identified that the average relative germination and survival rates of wheat with the BADH-A1b allele were significantly higher than wheat with the BADH-A1a allele. The results reveal that wheat containing BADH-A1b has stronger drought and salt tolerance than wheat with BADH-A1a. Meanwhile, the geographic distribution and frequency of the TaBADH-A1 locus alleles indicate that BADH-A1a has been preferred in Chinese wheat breeding programs, while BADH-A1b, associated with favorable stress tolerance, has been neglected. The results of this study provide evidence for an excellent candidate allele for marker-assisted selection of new wheat cultivars with increased salt tolerance and drought resistance.