Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson’s correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R² of 0.86, RMSE of 0.23 kg·m⁻², MAE of 0.16 kg·m⁻², and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB.

High-density planting is an effective technique to optimize yields of mulched cotton. On the other hand, deficit irrigation is an emerging water-saving strategy in cotton cultivation, especially suitable for arid and water-scarce areas. However, the relationships between deficit irrigation, high-density planting, and regulation mechanisms of canopy light radiation and light use efficiency (LUE) in cotton is not yet clear. To clarify the mechanism of light interception (LI) and the LUE of cotton canopies, three irrigation treatments [315 (50% Fc), 405 (75% Fc, farmers’ irrigation practice), and 495 mm (100% Fc), where Fc was the field capacity] with three plant densities [13.5, 18.0 (farmers’ planting practice), and 22.5 plants m²] were applied. The findings of this research revealed that, under deficit irrigation, the above-ground dry matter (ADM) was reduced by 5.05% compared to the farmers’ irrigation practice. Over both years and across all plant densities, LI and LUE under deficit irrigation decreased by 8.36% and 4.79%, respectively, relative to the farmers’ irrigation practices. In contrast, LI and LUE for the highest irrigation level increased by 10.59% and 5.23%, respectively. In the case of the interaction (plant density and irrigation level), the ADM under deficit irrigation and high-density combination increased by 7.69% compared to the control (farmers’ irrigation × sowing practices interaction effects). The LI and LUE also exhibited an increase in 1.63% and 6.34%, respectively. Notably, the LI effect of the middle and upper cotton canopy under film drip irrigation reached 70%. A lower irrigation level resulted in a higher percentage of LI in the lower canopy region. The leaf area index, light interception rate, and extinction coefficient escalated with the increase in plant density. Under deficit irrigation treatment, the LI of the 0–30 cm canopy in high plant density settings increased by 8.6% compared to the control (farmers’ irrigation × sowing practices interaction effects). In conclusion, deficit irrigation and increased plant density improved the interception of LI and LUE of cotton canopy. These findings may help the farmers to optimize their agricultural management strategies in water-deficient areas.

Cotton (Gossypium hirsutum L.), as a significant economic crop, has undergone significant modernization in planting methods, and its smart irrigation management relies heavily on accurate cotton water content (CWC) estimation. Existing ground-based methods for measuring CWC are constrained by their limited scope and high monitoring costs. Although the development of unmanned aerial vehicle (UAV) technology has provided a new opportunity for large-scale CWC measurements, there remains a gap in the study of CWC estimation in cotton using multi-source and multi-stage data. In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. The findings demonstrate that in a single growth stage, the boll setting stage performs the best, and multi-source and multi-stage inputs can improve the accuracy of CWC estimation, with the best performance of XGB (R² = 0.860). Overall, this study highlights that the synergistic use of multi-source and multi-stage data can effectively improve CWC estimation in cotton, suggesting UAV-based data will lead to a brighter future for precision agriculture.

The accurate estimation of aboveground biomass (AGB) is essential for monitoring crop growth and supporting precision agriculture. Traditional AGB estimation methods relying on single spectral indices (SIs) or statistical models often fail to address the complexity of vertical canopy stratification and growth dynamics due to spectral saturation effects and oversimplified structural representations. In this study, a unmanned aerial vehicle (UAV) equipped with a 10-channel multispectral sensor was used to collect spectral reflectance data at different growth stages of cotton. By integrating multiple vegetation indices (VIs) with three algorithms, including random forest (RF), linear regression (LR), and support vector machine (SVM), we developed a novel stratified biomass estimation model. The results revealed distinct spectral reflectance characteristics across the upper, middle, and lower canopy layers, with upper-layer biomass models exhibiting superior accuracy, particularly during the middle and late growth stages. The coefficient of determination of the UAV-based hierarchical model (R² = 0.53–0.70, RMSE = 1.50–2.96) was better than that of the whole plant model (R² = 0.24–0.34, RMSE = 3.91–13.85), with a significantly higher R² and a significantly lower root mean squared error (RMSE). This study provides a cost-effective and reliable approach for UAV-based AGB estimation, addressing limitations in traditional methods and offering practical significance for improving crop management in precision agriculture.

The optimization of plant density plays a crucial role in cotton production, and deficit irrigation, as a water-saving measure, has been widely adopted in arid regions. However, regulatory mechanisms governing nitrogen absorption, transportation, and nitrogen use efficiency (NUE) in cotton under deficit irrigation and high plant density remain unclear. To clarify the mechanisms of N uptake and NUE of cotton, the main plots were subjected to three irrigation amounts based on field capacity (Fc): (315 [W1, 0.5 Fc], 405 [W2, 0.75 Fc, farmers’ irrigation practice], and 495 mm [W3, 1.0 Fc]). Subplots were planted and applied at three densities: (13.5 [M1], 18.0 [M2, farmers’ planting practice], and 22.5 [M3] plants m⁻²). The results revealed that under low-irrigation conditions, the cotton yield was 5.1% lower than that under the farmer’s irrigation practice. In all plant densities and years, the nitrogen uptake of cotton increased significantly with the increase in irrigation. However, excessive irrigation resulted in nitrogen accumulation and migration, mainly concentrated in the vegetative organs of cotton, which reduced the NUE by 9.2% compared with that under farmers’ irrigation practice. Concerning the interaction between irrigation and plant density, under low irrigation, the nitrogen uptake of high-density planting was higher, and the yield of seed cotton was only 2.9% lower than that of the control (the interaction effect of farmers’ irrigation × plant density), but the NUE was increased by 10.9%. Notably, with the increase in irrigation amount, the soil nitrate nitrogen at the 0–40 cm soil layer decreased, and high irrigation amounts would lead to the transfer of soil nitrate nitrogen to deep soil. With the increase in plant density, the rate of nitrogen uptake and the amount of nitrogen uptake increased, which significantly reduced the soil nitrate nitrogen content. In conclusion, deficit irrigation and high plant density can improve cotton yield and NUE. We anticipate that these findings will facilitate optimized agricultural management in areas with limited water.

Unmanned Aerial Vehicles (UAVs) offer enhanced spatial and temporal resolution for agricultural remote sensing, surpassing traditional satellite-based methods. Given the abundance of evolving machine-learning methods for crop recognition, this study evaluates and compares five machine learning algorithms (ML) and tests an Ensemble Learning method as a sixth approach, integrated with object-based image analysis (OBIA) for crop-type classification using UAV multispectral imagery, aiming to identify the most effective model and produce a classification map based on the best-performing method. Image segmentation was built using eCognition software, and spectral, index, and gray level co-occurrence matrix (GLCM) features were extracted from the segmented object. A machine learning model integrating multiple classification algorithms (SVM, ANN, RF, XGBoost, KNN, Ensemble Learning) with automated hyperparameter optimization was developed and executed in Google Colab using Python 3.10. All classifiers achieved accuracies exceeding 80% and Area Under the Curve (AUC) values above 0.9. SVM and ANN are the best classifiers, with the same value of accuracy (94%), followed by XGBoost (93%), RF (92%), and KNN (89%). The Ensemble Learning method (SVM + ANN) as a sixth approach outperformed all single models, with an accuracy value of 95%. Cotton, maize, peanut, and soybean were classified with the highest accuracy, with index and GLCM features contributing most significantly, followed by spectral features. The integration of high-resolution UAV imagery with ML and OBIA demonstrates strong potential for automated crop-type classification, offering valuable support for precision agriculture applications.