Cotton, as a globally important fiber crop, is significantly affected by drought stress during production. This study uses the drought-resistant variety Jin and the drought-sensitive variety TM-1 as test materials. Through multi-period drought stress treatments at 0 d, 7 d, 10 d, 15 d, and 25 d, combined with dynamic monitoring of physiological indicators, RNA sequencing, and weighted gene co-expression network analysis, the molecular mechanism of cotton drought resistance is systematically analyzed. Dynamic monitoring of physiological indicators showed that Jin significantly accumulated proline, maintained superoxide dismutase activity, reduced malondialdehyde accumulation, and delayed chlorophyll degradation. Transcriptome analysis revealed that Jin specifically activated 8544 differentially expressed genes after stress, which were significantly enriched in lipid metabolism (α-linolenic acid, ether lipids) and secondary metabolic pathways. Weighted gene co-expression network analysis identified co-expression modules significantly correlated with proline (r = 0.81) and malondialdehyde (r = 0.86) and selected the key hub gene Gh_A08G154500 (WRKY22), which was expressed 3.2 times higher in Jin than in TM-1 at 15 days of drought stress. Functional validation suggested that WRKY22 may form a “osmotic regulation–membrane protection” co-regulatory network by activating Pro synthesis genes (P5CS) and genes involved in the jasmonic acid signaling pathway. This study reveals, for the first time, the possible dual regulatory mechanism of WRKY22 in cotton’s drought resistance, providing a theoretical basis for cotton drought-resistant breeding.

Drought stress significantly affects the growth, development, and yield of cotton, triggering the response of multiple genes. Among them, ascorbate peroxidase (APX) is one of the important antioxidant enzymes in the metabolism of reactive oxygen species in plants, and APX enhances the ability of plants to resist oxidation, thus increasing plant stress tolerance. Therefore, enhancing the activity of APX in cells is crucial to improving plant stress resistance. Previous studies have isolated differentially expressed proteins under drought stress (GhAPX7) in drought-resistant (KK1543) and drought-sensitive (XLZ26) plants. Thus, this study analyzed the expression patterns of GhAPX7 in different cotton tissues to verify the drought resistance function of GhAPX7 and explore its regulatory pathways. GhAPX7 had the highest expression in cotton leaves, which significantly increased under drought stress, suggesting that GhAPX7 is essential for improving antioxidant capacity and enzyme activities in cotton. GhAPX7 silencing indirectly affects pronounced leaf yellowing and wilting in drought-resistant and drought-sensitive plants under drought stress. Malondialdehyde (MDA) content was significantly increased and chlorophyll and proline content and APX enzyme activity were generally decreased in silenced plants compared to the control. This result indicates that GhAPX7 may improve drought resistance by influencing the contents of MDA, chlorophyll, proline, and APX enzyme activity through increased expression levels. Transcriptome analysis revealed that the drought-related differentially expressed genes between the control and treated groups enriched plant hormone signal transduction, MAPK signaling, and plant–pathogen interaction pathways. Therefore, the decreased expression of GhAPX7 significantly affects the expression levels of genes in these three pathways, reducing drought resistance in plants. This study provides insights into the molecular mechanisms of GhAPX7 and its role in drought resistance and lays a foundation for further research on the molecular mechanisms of response to drought stress in cotton.

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm re-sources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Sim-ultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coef-ficient with the defoliation rate; and when the PSRI value is higher, the defoliation ef-fect of the material is better. Using drone multispectral technology, 15 defoli-ant-sensitive cotton materials were identified, with defoliation rates exceeding 85%, boll opening rates ranging from 76.67% to 98.04%, and PSRI values between 0.1607 and 0.1984.(4) Conclusions: The study found that vegetation indices with sensitive re-sponses can serve as effective indicators for evaluating the sensitivity of cotton breed-ing materials to defoliants. The combined analysis of traditional survey methods and PSRI classification demonstrates that using drone multispectral technology as a sub-stitute for manual methods in large-scale, rapid monitoring and selection of cotton breeding materials with excellent defoliation effects is feasible.

Cotton, as a globally important fiber crop, is significantly affected by drought stress during production. This study uses the drought-resistant variety Jin and the drought-sensitive variety TM-1 as test materials. Through multi-period drought stress treatments at 0d, 7d, 10d, 15d, and 25d, combined with dynamic monitoring of physiological indicators, RNA sequencing (RNA-Seq), and weighted gene co-expression network analysis (WGCNA), the molecular mechanism of cotton drought resistance is systematically analyzed. Dynamic monitoring of physiological indicators showed that Jin significantly accumulated proline, maintained superoxide dismutase (SOD) activity, reduced malondialdehyde (MDA) accumulation, and delayed chlorophyll degradation. Transcriptome analysis revealed that Jin specifically activated 8,544 differentially expressed genes after stress, which were significantly enriched in lipid metabolism (α-linolenic acid, ether lipids) and secondary metabolic pathways. WGCNA identified co-expression modules significantly correlated with proline (r=0.81) and MDA (r=0.86) and selected the key hub gene Gh_A08G154500 (WRKY22), which was expressed 3.2 times higher in Jin than in TM-1 at 15 days of drought stress. Functional validation suggested that WRKY22 may form a "osmotic regulation-membrane protection" co-regulatory network by activating proline synthesis genes (P5CS) and genes involved in the jasmonic acid signaling pathway. This study reveals, for the first time, the possible dual regulatory mechanism of WRKY22 in cotton's drought resistance, providing a theoretical basis for cotton drought-resistant breeding.

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Simultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coefficient with the defoliation rate; and when the PSRI value is higher, the defoliation effect of the material is better. By comparing the traditional investigation method with the unmanned aerial vehicle multispectral technology, 15 cotton materials sensitive to defoliants were determined, with a defoliation rate of over 85%, a lint percentage ranging from 76.67% to 98.04%, and a PSRI value ranging from 0.1607 to 0.1984. (4) Conclusions: The study found that the vegetation index with sensitive response can be used as an effective indicator to evaluate the sensitivity of cotton breeding materials to defoliants. Using an unmanned aerial vehicle (UAV) equipped with vegetation indices for screening shows a high consistency with the manual investigation and screening method in screening excellent defoliation materials; it proves that it is feasible to screen cotton breeding materials with excellent defoliation effects using UAV multispectral technology.

To address the challenges of large-scale drought-resistance phenotyping in cotton—specifically the difficulty of assessment and the high cost of destructive sampling—this study developed a high-throughput evaluation framework for the Drought Resistance Coefficient of Effective Boll Number (DCEBN) in Gossypium hirsutum (upland cotton). This framework integrates time-series UAV-based multispectral technology with interpretable machine learning algorithms. Based on field drought experiments conducted in Xinjiang during 2023–2024 using 150 core germplasm accessions, we extracted and calculated the relative response ratios of canopy multispectral vegetation indices (DCVIs). We compared the predictive performance of K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), and Stacking ensemble algorithms, while employing the SHapley Additive exPlanations (SHAP) framework to quantify the contribution of each vegetation index to DCEBN prediction. This framework provides a practical, non-destructive approach for large-scale DCEBN prediction and for identifying key spectral indicators of drought tolerance in cotton. The results demonstrated that time-series multispectral features sensitively captured the dynamic response of cotton to water deficit; notably, ensemble learning models (RF and Stacking) exhibited superior fitting accuracy and generalization capabilities in predicting DCEBN. SHAP analysis identified NRI, SCCCI, MTVI2, and EVI as the four key indices contributing most significantly to the predictions, offering interpretable guidance for reducing spectral redundancy in future phenotyping. Validation using extreme-phenotype genotypes further confirmed that changes in these four indices effectively reflected the decline in photosynthetic rate and the reduction in effective boll number under drought stress, demonstrating that the model can both predict DCEBN and identify extreme germplasm for breeding use.