Background: The sharp increase of plant genome and transcriptome data provide valuable resources to investigate evolutionary consequences of gene duplication in a range of taxa, and unravel common principles underlying duplicate gene retention. Results: We survey 141 sequenced plant genomes to elucidate consequences of gene and genome duplication, processes central to the evolution of biodiversity. We develop a pipeline named DupGen_finder to identify different modes of gene duplication in plants. Genes derived from whole-genome, tandem, proximal, transposed, or dispersed duplication differ in abundance, selection pressure, expression divergence, and gene conversion rate among genomes. The number of WGD-derived duplicate genes decreases exponentially with increasing age of duplication events—transposed duplication- and dispersed duplication-derived genes declined in parallel. In contrast, the frequency of tandem and proximal duplications showed no significant decrease over time, providing a continuous supply of variants available for adaptation to continuously changing environments. Moreover, tandem and proximal duplicates experienced stronger selective pressure than genes formed by other modes and evolved toward biased functional roles involved in plant self-defense. The rate of gene conversion among WGD-derived gene pairs declined over time, peaking shortly after polyploidization. To provide a platform for accessing duplicated gene pairs in different plants, we constructed the Plant Duplicate Gene Database. Conclusions: We identify a comprehensive landscape of different modes of gene duplication across the plant kingdom by comparing 141 genomes, which provides a solid foundation for further investigation of the dynamic evolution of duplicate genes.

Background: The cytochrome P450 (CYP) superfamily is the largest enzyme metabolism family in plants identified to date, and it is involved in many biological processes, including secondary metabolite biosynthesis, hormone metabolism and stress resistance. However, the P450 gene superfamily has not been well studied in pear (Pyrus spp.). Results: Here, the comprehensive identification and a comparative analysis of P450 superfamily members were conducted in cultivated and wild pear genomes. In total, 338, 299 and 419 P450 genes were identified in Chinese white pear, European pear and the wild pear, respectively. Based on the phylogenetic analyses, pear P450 genes were divided into ten clans, comprising 48 families. The motif and gene structure analyses further supported this classification. The expansion of the pear P450 gene family was attributed to whole-genome and single-gene duplication events. Several P450 gene clusters were detected, which have resulted from tandem and proximal duplications. Purifying selection was the major force imposed on the long-term evolution of P450 genes. Gene dosage balance, subfunctionalization and neofunctionalization jointly drove the retention and functional diversification of P450 gene pairs. Based on the association analysis between transcriptome expression profiles and flavonoid content during fruit development, three candidate genes were identified as being closely associated with the flavonoid biosynthesis, and the expression of one gene was further verified using qRT-PCR and its function was validated through transient transformation in pear fruit. Conclusions: The study results provide insights into the evolution and biological functions of P450 genes in pear.

To realize autonomous navigation and intelligent management in orchards, vehicles require real-time positioning and globally consistent mapping of surroundings with sufficient information. However, the unstructured and unstable characteristics of orchards present challenges for accurate and stable localization and mapping. This study proposes a framework fusing LiDAR, visual, and inertial data by using the extended Kalman filter (EKF) to achieve real-time localization and colorful LiDAR point-cloud mapping in orchards. First, the multi-sensor data were integrated into a loosely-coupled framework based on the EKF to improve the pose estimation, with the pose estimation from LiDAR and gyroscope acting as the predictions, while that from visual-inertial odometry acting as the observations. Then, the Loam_Livox algorithm was enhanced by incorporating color from the image into the LiDAR point cloud, enabling the real-time construction of a three-dimensional colorful map of the orchard. The method demonstrates a high accuracy for localization in different motion trajectories (average RMSE: 0.3436) and different scenarios (average RMSE: 0.1230) and clear and efficient construction of three-dimensional colorful mapping, taking only 75.01 ms in localization and mapping for a frame of LiDAR point cloud. This indicates the proposed method has a great potential for the autonomous navigation of agricultural vehicles.

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch–leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precision_sem, mean Recall_sem, mean F1-score, and mean Intersection over Union (IoU) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precision_ins, Recall_ins, and mean coverage (mCoV) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm²), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management.