Leaf senescence reduces the photosynthetic capacity of leaves, thus significantly affecting the growth, development, and yield formation of cotton. Melatonin (MT) is a multipotent substance proven to delay leaf senescence. However, its potential mechanism in delaying leaf senescence induced by abiotic stress remains unclear. This study aimed to explore the effect of MT on delaying drought-induced leaf senescence in cotton seedlings and to clarify its morphological and physiological mechanisms. Drought stress upregulated the leaf senescence marker genes, destroyed the photosystem, and led to excessive accumulation of reactive oxygen species (ROS, e.g., H₂O₂ and O₂⁻), thus accelerating leaf senescence. However, leaf senescence was significantly delayed when 100 μM MT was sprayed on the leaves of the cotton seedlings. The delay was embodied by the increased chlorophyll content, photosynthetic capacity, and antioxidant enzyme activities, as well as decreased H₂O₂, O₂⁻, and abscisic acid (ABA) contents by 34.44%, 37.68%, and 29.32%, respectively. MT significantly down-regulated chlorophyll degradation-related genes and senescence marker genes (GhNAC12 and GhWRKY27/71). In addition, MT reduced the chloroplast damage caused by drought-induced leaf senescence and maintained the integrity of the chloroplast lamellae structure under drought stress. The findings of this study collectively suggest that MT can effectively enhance the antioxidant enzyme system, improve photosynthetic efficiency, reduce chlorophyll degradation and ROS accumulation, and inhibit ABA synthesis, thereby delaying drought-induced leaf senescence in cotton.

Late-sowing and high-density cultivation are typical cotton planting models that are widely applied in the Yellow River Valley of China. The model can easily lead to late maturity and reduced yields. Plant topping and ethephon spraying have been shown to improve cotton earliness and yields. However, the optimal topping date and ethephon concentration are yet to be established. A two-year field experiment (2020–2021) was thus conducted to assess the effects of spraying high-concentration ethephon and early topping on the growth, yield, and earliness of cotton under late-sowing and high-density cultivation models. The experiment employed a split-plot design, in which the main plots were sprayed with varying ethephon concentrations (2000 mL/ha, E₂₀₀₀; 4000 mL/ha, E₄₀₀₀; 6000 mL/ha, E₆₀₀₀), while subplots were plant topping dates (T13, 13 July, early-topped; T20, 20 July, suitable-topped; T27, 27 July, late-topped). Higher ethephon concentrations (E₄₀₀₀) increased the earliness of early-topped (T13) plants. Boll-setting and boll-opening rates increased by 12.00% and 16.83% in T13 plants, respectively, relative to T27 plants. However, the shedding rate for T13 plants decreased by 12.00%. Earliness and harvest index (HI) for the T13 plants were higher than those of the T27 plants by 8.01% and 18.91%, respectively. Even though the T13 plant yield decreased slightly, their boll weight increased by 3.99% and 12.27%, respectively, relative to those of the T20 and T27 plants. Earliness increased with increasing ethephon concentration. In conclusion, early topping and higher ethephon concentrations can effectively improve cotton yield and earliness.

Against the backdrop of global population and food security challenges, improving crop nitrogen use efficiency (NUE) is essential for sustainable agriculture. Conventional nitrogen fertilizers suffer from low utilization rates and significant environmental pollution. In contrast, controlled-release nitrogen fertilizers (CRNFs) synchronize nutrient supply with crop demand, offering significant advantages in enhancing yield, efficiency, and environmental sustainability. This review systematically outlines the developmental types of CRNFs, with a focus on their agronomic and ecological benefits. Key quantitative outcomes include yield increases of 3.0–15.3% in winter wheat, 12.38–22.67% in cotton, and maintained or improved maize yield even with a 20% reduction in nitrogen input. CRNFs also reduce ammonia volatilization by 20–43% in paddy fields. The review further elucidates the synergistic mechanisms through which CRNFs optimize root growth, enhance photosynthetic efficiency, and improve NUE. Major challenges such as high costs, release control precision, and coating material sustainability are critically assessed. Future directions include developing biodegradable coatings, smart fertilization systems, and stronger policy frameworks to facilitate broader adoption. This work provides a comprehensive theoretical and practical foundation for advancing the efficient and sustainable use of CRNFs in modern crop production.

To address low nitrogen use efficiency (NUE) derived from excessive fertilization in cotton production in the Yellow River Basin, a field study was conducted to evaluate the effects of two planting densities and six nitrogen (N) rate levels. Key results show that a N rate of 225 kg ha⁻¹ optimized root length density and root biomass density. High planting density (105,000 plants ha⁻¹) improved the population-level root traits, photosynthetic radiation interception, and boll number per unit area, though it reduced individual plant root development. Total dry matter peaked at 225 kg ha⁻¹ N, and density increased reproductive dry matter by 7.5–11.9%. Higher N rates reduced reproductive partitioning and root–shoot ratio. While the maximum seed cotton yield (SCY) was 225 kg ha⁻¹, near-maximum yield was achieved at 150 kg ha⁻¹. NUE declined with increasing N, but densification improved agronomic NUE and partial factor productivity by 1.5–6.6% and 3.3–39.3%, respectively. Under the “densification with N reduction” mode, combining a planting density of 105,000 plants·ha⁻¹ with an N rate of 150 kg·ha⁻¹ achieved conventional yield. At the same density, an N rate of 225 kg·ha⁻¹ not only enabled high yield and maintained relatively high NUE but also showed better adaptability to the simplified cultivation mode in Yellow River Basin cotton-growing regions.

Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSI_VI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSI_VI) and AGB was enhanced by 13.60% overall, with the CGSI_CIre exhibiting the highest correlation with cotton AGB (R² = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSI_VI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field.

Root phenotypic analysis is closely related to crop yield and stress resistance. Although deep learning can improve the efficiency of root phenotype recognition, existing methods suffer from insufficient segmentation accuracy under complex soil backgrounds and focus on a single target. To address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network. The WaveUNet+ model is based on U-Net3plus, replaces traditional downsampling with the Haar wavelet transform, and introduces the EMA module. The impact of the wavelet transform is validated using Grad-CAM, and HD95 is employed to evaluate the improvement in segmentation quality brought by the attention mechanism from the perspective of boundary accuracy. Transfer learning is used to improve model generalization, and the test results on diverse roots and various soils are compared. A Docker containerized root image segmentation method is designed to achieve convenient and practical operation, and the deployment feasibility of the model on edge devices is also verified. Our model effectively enhances the recognition of fine roots in soil backgrounds, leading to improvements across various metrics, achieving an Accuracy of 99.2%, while improving model accuracy with relatively low parameter count and model size. Compared with the original U-Net model, mIoU is increased by 1.52% and Recall by 2.93%. The results show that the model not only performs excellently on the original dataset but also maintains good generalization ability across different imaging modalities, crop species, and soil conditions. With Docker, users can achieve root image segmentation on their own computers without tedious program installation and environment configuration. In the future, we will attempt methods such as pruning and quantization to reduce model size, so as to better adapt to the deployment requirements of edge devices.