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The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on food crops, less research has been reported on the application to fruit trees, especially citrus trees. In addition, most studies have used single-modal data for modeling, but some studies have shown that multi-modal data can be effective in improving experimental results. This study utilizes data collected from a UAV platform, including RGB images and point cloud data, to construct single-modal regression models named VoVNet (using RGB data) and PCNet (using point cloud data), as well as a multi-modal regression model called VPNet (using both RGB data and point cloud data). The LAI of citrus trees was estimated using deep neural networks, and the results of two experimental hyperparameters (loss function and learning rate) were compared under different parameters. The results of the study showed that VoVNet had Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R2) of 0.129, 0.028, and 0.647, respectively. In comparison, PCNet decreased by 0.051 and 0.014 to 0.078 and 0.014 for MAE and MSE, respectively, while R2 increased by 0.168 to 0.815. VPNet decreased by 0% and 42.9% relative to PCNet in terms of MAE and MSE to 0.078 and 0.008, respectively, while R2 increased by 5.6% to 0.861. In addition, the use of loss function L1 gave better results than L2, while a lower learning rate gave better results. It is concluded that the fusion of RGB data and point cloud data collected by the UAV platform for LAI estimation is capable of monitoring citrus trees' growth process, which can help farmers to track the growth condition of citrus trees and improve the efficiency and quality of orchard management. [ABSTRACT FROM AUTHOR]

Introduction: In the Dongting water system, the Carassius auratus (Crucian carp) complex is characterized by the coexistence of diploid forms (2n=100, 2nCC) and polyploidy forms. The diploid (2nCC) and triploid C.auratus (3n=150, 3nCC) had the same fertility levels, reaching sexual maturity at one year., Methods: The nucleotide sequence, gene expression, methylation, and immunofluorescence of the gonadotropin releasing hormone 2( Gnrh2 ), Gonadotropin hormone beta( Gthβ ), and Gonadotropin-releasing hormone receptor( Gthr ) genes pivotal genes of the hypothalamic-pituitary-gonadal (HPG) axis were analyzed., Results: The analysis results indicated that Gnrh2 , follicle-stimulating hormone receptor( Fshr ), and Lethal hybrid rescue( Lhr ) genes increased the copy number and distinct structural differentiation in 3nCC compared to that in 2nCC. The transcript levels of HPG axis genes in 3nCC were higher than 2nCC (P<0.05), which could promote the production and secretion of sex steroid hormones conducive to the gonadal development of 3nCC. Meanwhile, the DNA methylation levels in the promoter regions of the HPG axis genes were lower in 3nCC than in 2nCC. These results suggested that methylation of the promoter region had a potential regulatory effect on gene expression after triploidization. Immunofluorescence showed that the localization of the Fshβ, Lhβ , and Fshr genes between 3nCC and 2nCC remained unchanged, ensuring the normal expression of these genes at the corresponding sites after triploidization., Discussion: Relevant research results provide cell and molecular biology evidence for normal reproductive activities such as gonad development and gamete maturation in triploid C. auratus , and contribute to further understanding of the genetic basis for fertility restoration in triploid C. auratus ., Competing Interests: Author QQ was employed by Hunan Yuelu Mountain Science and Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Xu, Yang, Deng, Xiao, Huang, Wang, Zhou, Luo, Zhang, Xu, Qin and Liu.)