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.

The balance between vegetative and reproductive growth is the central objective in the cotton production system, which is associated with cotton maturity and yield. In China, manual topping (MT) has been performed many years prior to or during the flowering period to inhibit vegetative growth and enhance reproductive growth. MT is gradually being replaced by chemical topping (CT) with mepiquat chloride (MC, 180 g ha⁻¹, 98% soluble powder) due to labor shortages and increasing labor cost. To determine whether CT influences cotton maturity and yield relative to MT, we carried out field experiments at four locations in the Yellow River Valley of China during 2018–2020. The results showed that CT did not alter the boll age, and although it produced taller and slender spatial boll distribution under several environments, it had little effect on the accumulation of boll fraction (the number of bolls in a given period divided by total boll number) over time at the end of blooming across locations. As a result, there were no significant differences between MT and CT in boll opening percentage in the late season. CT did not influence yield or yield components, except under severe drought. Therefore, CT with MC (180 g/ha, 98% soluble powder) during the flowering period will not compromise the maturity or yield of cotton in the Yellow River Valley of China. Similar outcomes would be achieved in other areas with similar ecological conditions and social conditions that require an alternative to extensive manual labor.

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.