[Objective] This research aims to analysis the function of GhWRKY44 gene under drought stress, and to provide candidate gene resources for drought-resistant breeding in cotton. [Methods] The coding sequence of GhWRKY44 gene was obtained by polymerase chain reaction (PCR) from the cDNA of CQJ-5 (Gossypium hirsutum) leaves. And bioinformatics analysis was performed. The expression pattern of GhWRKY44 gene under the treatment of abscisic acid (ABA) and polyethylene glycol (PEG) 6000 were analyzed by quantitative real-time PCR (qRT-PCR). The function of GhWRKY44 genes under drought stress was investigated by using the virus-induced gene silencing (VIGS) technology. [Results] The protein encoded by GhWRKY44 is a member of class Ⅰa WRKY, and is closely related to GbWRKY44. The expression of GhWRKY44 was induced by PEG 6000 and ABA. Compared with the control cotton plants, GhWRKY44 silenced cotton plants showed more severe leaf wilting, and plant survival rate and leaf chlorophyll content (soil and plant analyzer development, SPAD value) were significantly reduced under drought stress. With 6 h and 7 h of dehydration treatment, the leaf water loss rate of GhWRKY44 silenced cotton plants was significantly higher than that of control plants. [Conclusion] Silencing of GhWRKY44 gene reduced drought tolerance of cotton, and GhWRKY44 is a positive regulator of drought tolerance in cotton.

[Objective] GbTMEM214 transgenic Gossypium hirsutum line, obtained using Agrobacterium-mediated method, was used to clarify the sequence characteristics and detection methods of the T-DNA insertion site, and further promote its biosafety evaluation. [Methods] Based on the genome resequencing technology, the sequencing data was compared with the G. hirsutum standard line TM-1 genome sequence by BLASTn, and specific primers were designed to verify the insertion site by polymerase chain reaction (PCR). [Results] The T-DNA carrying the target gene GbTMEM214 was integrated into the position of 57 019 068-57 019 106 bp on chromosome D13 of G. hirsutum genome, resulting in 37 bp deletion of cotton genome. Combined with the flanking sequence of T-DNA insertion site obtained by PCR amplification, the specific detection method for GbTMEM214 transgenic cotton was established. [Conclusion] The T-DNA insertion site and flanking sequence of GbTMEM214 transgenic cotton was obtained based on genome resequencing technology, which can provide technical reference for biosafety evaluation of the transgenic cotton.

[Objective] Assessing cotton growth status through chlorophyll content offers a swift, accurate, and extensive monitoring of cotton development, which aids in precision farming. [Methods] To enhance the accuracy of chlorophyll content evaluation in cotton, fractional-order differentiation ranging from 0 to 2(with a step size of 0.2) and wavelet transform within scales from 1 to 10 to process the hyperspectral reflectance data collected from both upland cotton and sea island cotton fields were employed. By analyzing the correlation between different spectral processing techniques and chlorophyll content, sensitive spectral bands were identified. Subsequently, support vector machine regression(SVR) and random forest regression (RFR) models were employed to construct hyperspectral estimation models for cotton chlorophyll content. [Results] (1) In the wavelength range from 325 to 1 075 nm, the spectral reflectance curves of the two cotton species show similar overall trends, with reflectance increasing with the increase in chlorophyll content.(2) Following continuous wavelet transform and fractional-order differentiation, the correlationship between hyperspectral data and chlorophyll content improved for both cotton species. Inversion models revealed that using RFR and wavelet energy coefficient 7 had the best results for upland cotton chlorophyll content estimation, with a coefficient of determination (R²) of 0.931, root mean square error (RMSE) of 0.782, and residual prediction deviation (RPD) of 2.162. Similarly, for sea island cotton, employing RFR and wavelet energy coefficient 6 resulted in the most effective chlorophyll content estimation, with the R² of 0.932, RMSE of 1.198, and RPD of 2.687. [Conclusion] This study provides technical insights for remotely estimating chlorophyll content in cotton plants.

[Objective] This study aims to explore the response of Gossypium hirsutum under artificial seawater and NaCl stresses, and to assess the feasibility of using artificial seawater to simulate salt stress for evaluating salt tolerance of cotton germplasms. [Methods] Utilizing 135 distinct G. hirsutum germplasms as the experimental materials, this study investigated the impact of artificial seawater and NaCl stresses on cotton seed germination and seedling growth. Through the integration of principal component analysis, membership function analysis, and cluster analysis methodologies, the comprehensive evaluation of cotton salt tolerance was conducted. The results of two identification methods were verified by field experiments under natural salt stress. [Results] The consistency of the identification results under the two salt stresses were only 52.38%, and there were great differences in the results. The identification results under artificial seawater stress were significantly and positively correlated with the results of field experiments, with a correlation coefficient of 0.720; while that under NaCl stress were not significantly correlated with the field identification results. Under artificial seawater and NaCl stress treatments, 21.90% and 33.33% of the 105 glandless cotton germplasms were resistant or tolerant to salt stress, respectively. Among them, Lu 17 and Handifen 29 showed strong salt tolerance under the two salt treatments. [Conclusion] Using artificial seawater that simulates the composition of coastal soil can identify the salt tolerance of cotton germplasms more accurately. Glandless cotton generally exhibits poorer salt tolerance, but there are still some germplasms with strong salt tolerance that can be used to breed new salt tolerant glandless cotton cultivars.

[Objective] This study aims to explore the prediction effects of different models on cotton plant height under high dense planting conditions in the Aral Reclamation Area, Xinjiang. [Methods] Xinluzhong 81 and Tahe 2, which are different in plant type, were used as experimental materials for field experiment under the high dense planting condition of 16 000·hm^-2 in Aral Reclamation Area. Prediction models for plant height growth were established using logistic, Gompertz, Richards growth equations, and decision tree machine learning methods using Python language. In addition, the prediction accuracy of the models was analyzed. [Results] For the logistic, Gompertz, and Richards models, the root mean square error (RMSE) of Xinluzhong 81 was 8.38%, 7.49%, and 7.52%, respectively, and the mean absolute error(MAE) was 6.80%, 5.79%, and 5.82%, respectively; the RMSE of Tahe 2 was 6.09%, 4.77%, and 4.85%, while the MAE was 4.52%, 3.34%, and 3.36%, respectively. The RMSE of Xinluzhong 81 and Tahe 2 by using decision tree machine learning method were 6.91% and 3.27%, respectively, and the MAE were 5.04% and 2.16%, respectively. The results indicated that logistic, Gompertz, and Richards growth equations and decision tree machine learning methods can effectively reflect the growth of cotton plant height under high dense planting condition. However, in terms of prediction accuracy, decision tree machine learning methods was generally superior to the three growth equations. [Conclusion] The machine learning method based on decision tree does not require mathematical and statistical knowledge to explain the model, training the model requires less data, and can achieve higher simulation accuracy. It has certain advantages in simulating cotton plant height, and is a beneficial supplement to the traditional growth equations.

[Objective] This paper aims to solve the problem of accurate recognition and localization of cotton with different postures and grades by cotton picker under the requirement of high-quality cotton picking. A cotton detection method YOLOX-Cotton based on the improved YOLOX is proposed. [Methods] YOLOX-Cotton uses YOLOX as the main framework, including a recognition module and a localization module, and incorporates coordinate attention (CA) module and SIoU loss function, and takes various posture and grade cotton pictures as data sets to train and test. [Results] The detection module of YOLOX-Cotton was capable of detecting cotton with different postures and grades, and the model precision, recall and average precision reached 92.9%, 86.8% and 92.4%, which were improved by 5.2, 5.5 and 6.1 percentage points, compared with the original YOLOX, respectively. The localization module of this model was capable of accurately obtaining the location of the cotton, the measurements were kept within the threshold range of the validated results of the field trial, and the standard deviation of all samples was less than 0.01. [Conclusion] The experiment proves that the YOLOX-Cotton can effectively solve the problem of cotton detection and localization by cotton picker under the requirement of high-quality cotton picking, and provides strong technical support for the realization of high-quality cotton picking.

[Objective] This study aims to reveal the effects of different irrigation treatments at the flowering and boll setting stage on the photosynthetic characteristics and yield of cotton, and to provide a reference for the optimization of irrigation system in cotton planting areas of northern Xinjiang. [Methods] A field experiment was conducted in Changji, Xinjiang in 2023, with CCRI 125 as the test variety. Three lower limits of irrigation were set at the flowering and boll setting stage, which were 55% field capacity (T1), 60% field capacity (T2), and 70% field capacity (T3), respectively. The local conventional drip irrigation mode was used as the control (CK). The effects of different treatments on the soil moisture content, photosynthetic characteristics, and yield traits of cotton during the flowering and boll setting stage were analyzed. And the correlation and regression relationships between the photosynthetic index, foliar temperature, and the meteorological factors were also explored. [Results] The soil moisture content of 0-60 cm soil layer of T3 treatment was maintained in a relatively high and stable range (18.5%-21.6%) during the flowering and boll setting period. During the early flowering and boll setting period (11 July), the daily average of net photosynthesis rate of T3 treatment was the highest, showing a daily trend of increasing-decreasing-ascending-decreasing. Correlation analyses showed that net photosynthetic rate and transpiration rate were positively correlated with 0-60 cm soil moisture content, foliar temperature, solar radiation intensity, and ambient temperature. Seed cotton yield and irrigation water use efficiency were the highest under T3 treatment, which were significantly increased by 26.46% and 71.43%, respectively, compared with that of CK. The multi-objective evaluation based on the rank-sum ratio method showed that T3 treatment had the best overall effect. [Conclusion] In the northern Xinjiang where water resources are scarce, the lower and upper limits of irrigation at the flowering and boll setting stage setting at 70% and 90% field capacity, respectively, is a reasonable water-saving and high-yield irrigation mode for cotton fields under the drip irrigation with plastic-film mulching.