InDels are the most common type of length polymorphism and play a critical role in the genetic traits of many important phenotypes in both plants and animals, making them an ideal source for length polymorphism molecular markers. However, in the process of cucumber breeding, researchers still face deficiencies in the identification of InDel loci and the development of genomic-wide molecular markers. In this study, we conducted InDel identification on 115 cucumber re-sequencing datasets, identifying a total of 7,842,946 InDels, with lengths ranging from 1 to 59 bp and an average density of one InDel every 2.8 kb on the chromosomes. The InDel variations were classified into four main categories, and 81 InDel hotspots were identified, serving as the foundation for constructing a cucumber InDel variation map. Additionally, we utilized an electronic PCR strategy to develop genome-wide InDel markers for cucumber, resulting in the selection of 22,442 InDel primers exhibiting high polymorphism (PIC ≥ 0.5) and major allele differences of ≥ 3 bp. We experimentally validated 50 randomly selected InDel primers, and the results showed that all markers exhibited high polymorphism. The construction of the cucumber genome InDel variation map aids in understanding the genetic basis of key traits in cucumber derived from InDel variations. The ideal InDel markers developed in this study may enhance the efficiency of cucumber breeding for resistance to both biotic and abiotic stresses, as well as scientific research.

Genome-wide association studies (GWAS) are a powerful approach for elucidating the genetic architecture of complex traits. The widespread application of GWAS across diverse crop species has generated vast amounts of GWAS summary statistics, creating a pressing need for effective tools to identify high-confidence quantitative trait loci (QTLs) from these data. Here, we present Candidate QTL Detector (CQD), a tool that enables the rapid and accurate identification of confident QTL regions from GWAS summary statistics. To evaluate the performance of CQD, we reanalyzed 64 phenotypic traits from a previously published maize diversity panel. CQD identified 108 high-confidence QTLs from 4179 significant GWAS signals, and these QTLs harbored several well-characterized genes, including NS2 (a regulator of tassel branch number), DRL1 (a key regulator of plant architecture), and ZmACS6 (an ACC synthase involved in ethylene biosynthesis), demonstrating the reliability of the QTLs detected by CQD. Overall, CQD provides an efficient and flexible framework for extracting robust QTLs from GWAS summary statistics, thereby facilitating the genetic improvement of complex traits and advancing crop functional genomics.

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production.

Accurately predicting the days to flowering for individual rapeseed plants is crucial for supporting operations such as emasculation, bagging, and pollination. However, traditional phenological prediction methods have rarely been applied at the individual plant level. To address this challenge, this study proposes a multimodal deep learning approach that integrates RGB images with future time-series meteorological data. First, the YOLOv8s-seg model segments rapeseed flower regions, reducing interference from complex field backgrounds, and data augmentation enhances data diversity. In the visual branch, five convolutional neural networks—MobileNet V2, MobileNet V3, ResNet18, EfficientNet-B0, and DenseNet 121—are compared. Results show that DenseNet 121 achieves the best balance between prediction accuracy and model complexity, and is selected as the visual backbone network. The meteorological branch uses a long short-term memory model to extract features from future time-series meteorological data, which are then fused with visual features via a multilayer perceptron. To address large-scale differences among meteorological variables, Z-score normalization is used to normalize the meteorological data. Experimental results show that the proposed model performs best on the test set, with a coefficient of determination of 0.8979, a root mean square error of 2.15 days, a mean absolute error of 1.51 days, and a ratio of performance to deviation of 3.13. Therefore, this study provides an effective method for predicting days to flowering in individual rapeseed plants, with potential applications in coordinating flowering periods between parental lines and supporting breeding decision-making.