Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R² of 0.912 and rRMSE of 15.387%, and the validation set performance had an R² of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R² of 0.901 and rRMSE of 16.318%, and the validation set performance had an R² of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture.

Under refined management, high-yield cotton fields are approaching their maximum output. However, how to break this yield upper limit, specifically the source–sink relationship is still inadequately researched. This experiment was conducted to explore the interaction mechanism between yield formation and source–sink parameters (photosynthesis, nitrogen content, canopy structure and dry matter accumulation and distribution). The treatments consisted of a no cutting source and sink treatment (CK), cutting 1/2 leaves per plant (1/2L) and cutting 1/2 bolls per plant (1/2B) at the initial flowering stage (IFS), the flower and boll stage (FABS), and the full boll stage (FBS). The results showed that 1/2L treatment minimized yield losses to 2.3–5.9% by enhancing photosynthetic compensation, with FBS-1/2L showing the smallest reduction (2.3–2.9%) due to higher leaf N content and SPAD values, whereas, the 1/2B treatments resulted in significant yield losses attributable to fewer bolls, especially the FBS-1/2B treatments, which reduced yields by 35.7–41.9%, with a compensatory rate of only 8.1–14.3%. It is noteworthy that the compensation rates of IFS-1/2B and FABS-1/2B could reach 26.7–32.3% and 18.7–23.8% of their yields due to the higher leaf N content. In a word, the source damage can be buffered by physiological compensation, while the sink loss leads to yield collapse due to the irreversibility of reproductive development. Thus, the core regulator of high-yield cotton fields was sink strength. Accordingly, optimizing the sink quality was performed through moderate boll thinning at the IFS, enhancing water and fertilizer supply at the FABS and strengthening sink organ protection at the FBS in order to realize a breakthrough in yield limit.

Timely monitoring of cotton defoliation progress is crucial for optimizing the quality of mechanical harvesting. To accurately assess the defoliation status prior to mechanical picking, a field experiment was conducted in Hejian, Hebei Province, China, in 2022. Using a DJI P4M multispectral drone, canopy images of cotton were collected before and after defoliation at three flight altitudes: 25 m, 50 m, and 100 m. The study employed machine learning algorithms including linear regression, Support Vector Machine (SVM), Generalized Additive Model (GAM), and Random Forest (RF) to invert the Leaf Area Index (LAI). Additionally, SVM-based supervised classification was introduced to eliminate background interference from soil and open cotton bolls, while the XGBoost model and SHAP method were used to analyze the main factors influencing LAI inversion. Key findings include the following: The univariate linear relationship between EVI and LAI proved to be the most robust, with the model constructed from 100 m flight altitude data performing best (validation set: R² = 0.921, RMSE = 0.284). The rate of LAI change showed a strong positive correlation with field-measured defoliation rate (r = 0.83–0.88), confirming its reliability as a proxy indicator for defoliation progress. Soil and open cotton bolls were identified as major negative factors affecting LAI inversion accuracy. The optimal machine learning prediction model varied with days after spraying, demonstrating significant temporal variability. This study demonstrates that high-throughput LAI inversion based on drone-derived multispectral EVI enables precise and dynamic monitoring of cotton defoliation. The approach provides farmers and field managers with an efficient, non-destructive monitoring tool. By delivering real-time insight into defoliation progress, it plays a pivotal role in enabling precision defoliation management, reducing excessive chemical use, optimizing the scheduling of mechanical operations, and ultimately enhancing both the sustainability and profitability of cotton production.