Cotton Research Institute

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R2 = 0.811; RMSE = 2.931 t ha−1). Texture features alone also showed strong predictive capability (R2 = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring.

Leaves, the primary photosynthetic organ in plants, provide critical insights into the genetic and epigenetic mechanisms underlying heterosis. This study systematically investigated the leaf phenotypic characteristics, gene expression profiles, and epigenetic regulation through methylation patterns, miRNAs and lncRNAs in two contrasting cotton hybrids, thereby establishing a novel multi-omics framework for heterosis analysis. Our primary innovation is the elucidation of expression level dominance (ELD) and its differential influence on heterotic performance. The Z41S hybrids display superior phenotypic heterosis, which correlates with heightened over-dominant (OD), under-dominant (UD), and paternal expressions (ELD-P). In contrast, the B985S hybrids are characterized by a predominance of maternal expression patterns (ELD-M). We identified CHH-type methylation variations as the major epigenetic regulators influencing dominant gene expression across both hybrids. Differentially methylated regions (DMRs), similarly methylated regions (SMRs), and transposable element distributions significantly impacted heterosis-related gene regulation, particularly through gene body modifications. Furthermore, the joint analysis of multi-omics data revealed that, nine genes including serine/threonine-protein kinase gene, zinc finger BED domain-containing protein RICESLEEPER 3/2, protein tyrosine kinase myosin-6 (Myh6), AGO2, RGA3, LRR and NB-ARC, subjected to multiple epigenetic regulation by miRNAs, lncRNAs, or DNA methylation, are poised to play a pivotal role in the manifestation of strong heterosis in cotton hybrids. These findings establish three fundamental contributions: (1) a comprehensive model integrating genomic and epigenetic regulation of cotton heterosis, (2) identification of parent-specific ELD patterns as molecular predictors of hybrid performance, and (3) characterization of CHH methylation as a critical epigenetic determinant in heterosis manifestation. Our results provide a transformative theoretical framework for optimizing hybrid breeding strategies through targeted manipulation of both genetic and epigenetic factors.

Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45-65%), W2 (55-75%), W3 (65-85%), W4 (75-95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7(650, 428), RSI8(663, 422), and RSI9(671, 450), performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R2, RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.

Upland cotton (Gossypium hirsutum), one of the world's major fiber crops, faces challenges from the genetic homogeneity of modern varieties. Here we present 107 gold-standard genome assemblies spanning the wild-to-domesticated continuum, revealing six large-scale structural variations, including a chromosomal reciprocal translocation and five inversions tracing the evolutionary history of cultivated cotton in the Americas. This history also involved continuous introgression from Gossypium barbadense, shaping the genetic diversity of G. hirsutum landraces and cultivars. Leveraging the graph pan-genome, we capture the sequence and structural diversity of nucleotide-binding site-leucine-rich repeat genes, uncovering pathogen-driven selection signatures and loci associated with disease resistance. A presence-absence variation genome-wide association study (GWAS) identified previously overlooked loci for key fiber traits, complementing single-nucleotide polymorphism-GWAS findings. Additionally, we construct a detailed map of large inversions, offering insights into hybridization dynamics and strategies to mitigate linkage drag. This study enhances our understanding of cotton evolution and domestication while delivering a valuable resource to enhance breeding.

BackgroundCotton is an important crop that provides a natural fiber source for the textile industry. Polyphenol oxidase (PPO) is a type of ubiquitous metalloproteinase in plants, which play crucial roles in regulating plant growth and development, as well as plant resistance against biotic and abiotic stresses. Although the whole genome sequence of Gossypium hirsutum L. (G. hirsutum) has long been published, this gene family has not yet been well studied in cotton.ResultsIn the present paper, we have identified the 51 PPO genes in cotton, and deeply analyzed 15 PPO genes in G. hirsutum, which are named GhPPOs. We found that three conserved domains - tyrosinase, DWL, and KFDV, were present in all 15 GhPPOs. We also analyzed the molecular characterization, phylogenetic relationships, gene structure, chromosome locations, 3D structure, and expression profiles of all the PPO genes identified. Based on the phylogenetic analysis, these PPO genes were classified into five groups (I, II, III, IV and V). The 15 GhPPO genes are distributed across eight chromosomes, and gene structure analysis showed that nine GhPPOs are devoid of introns. Analysis of cis-acting elements in GhPPOs promoters indicated their potential fundamental roles in response to plant growth, development, and stresses. Transcript profiling and RT-qPCR analysis revealed that GhPPO3/11 showed high expression levels during cotton fibre elongation, and GhPPO3/5 can be induced in response to cotton bollworm infestation. Moreover, the 3-dimensional structures of GhPPO3/ 11 and GhPPO3/ 5 were predicted by homolog modeling, and we found that all of them possess similarity spatial structures, with two independent domains, which could be the structural basis for performing biological functions.ConclusionsThese findings not only provide valuable insights into the phylogenetic relationships, gene structure, cis-acting elements, and functional characteristics of upland cotton PPOs, but also shed light on their potential roles in cotton fibre elongation and response to biotic stresses.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12864-025-11906-1.

In order to realize the nondestructive and rapid detection of fat content of Xanthoceras sorbifolia and meet the screening of breeding materials and industrial processing requirements of X.sorbifolia, 46 X.sorbifolia were selected as the standard sample set, the results showed that the fat content of 46 apricot kernel kernels was 49.38%~68.98% an average content of 61.62%. The fat content of the seed kernel was determined by the Soxhlet extraction method, and the spectral data of the sample was collected by the near-infrared spectroscopy (NIRS) technology, and the Unscrambler software was used to construct the NIRS prediction model of X.sorbifolia fat content by the partial least squares (PLS) method. The results showed that the regression curve R-Square (determination coefficient) of the model was 0.9856, and the RMSE (standard error) was 0.4149, which could be used for effective prediction. At the same time, 32 X.sorbifolia samples not participating in the modeling were selected as validation materials to further carry out external test on the prediction effect of the model. The results showed that the external test regression curve R-Square was 0.9014, RMSE was 0.8259, and the predicted value of fat content was in good agreement with the chemical value.