Improving pesticide application efficiency in dense pepper canopies requires optimization of Unmanned Aerial Vehicle (UAV) operational parameters based on both droplet deposition and pest control performance. This study evaluated the effects of flight height and nominal droplet size setting on canopy deposition characteristics and pest control efficacy using a DJI T60 plant protection UAV under field conditions. Three flight heights (2, 3, and 4 m) and five nominal droplet size settings (100, 150, 200, 250, and 300 µm) were investigated. Droplet coverage, deposition density, canopy penetration rate, deposition uniformity, and corrected control efficacy were used as evaluation indicators, and TOPSIS was applied for comprehensive parameter optimization. The results showed that both flight height and nominal droplet size setting exert a certain influence on droplet deposition characteristics and pest control efficacy. Under the specific conditions tested in this study, the 3 m flight height achieved favorable overall deposition performance, while the 250 µm nominal setting exhibited the highest effective coverage among the tested droplet size treatments. Pest control efficacy showed a consistent trend with droplet deposition performance, and the 3 m + 250 µm nominal setting combination achieved the highest corrected control efficacy at 7 DAT. According to the TOPSIS evaluation, the combination of 3 m flight height and 250 µm nominal droplet size setting achieved the best overall performance under the experimental conditions. Because actual airborne droplet size spectra were not independently measured, the recommendation refers to the tested operational setting rather than a measured droplet size population.

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing.

To address the plant protection challenges of high canopy closure and poor droplet penetration in high-density maize, this study systematically elucidated the synergistic regulatory mechanisms of UAV operational parameters and reduced nano-pesticide application. Integrating laboratory microscopic characterization with a two-phase field experiment, this study evaluated spray volume, droplet size, and pesticide dosage as core variables. Phase I quantified the effects of physical spray parameters on droplet deposition and penetration characteristics, while Phase II comprehensively revealed the field control efficacy under multi-factor synergy. Results showed that 22.5 L/ha achieved the optimal balance; it increased deposition density by 109.0% compared to 15.0 L/ha (p < 0.05) while maintaining comparable efficacy to 30.0 L/ha without sacrificing operational efficiency. The 200 μm droplets demonstrated superior penetration, increasing lower-layer coverage by 33.9% compared to 300 μm (p < 0.05), and overcoming the evaporation limitations of 100 μm (which showed no significant efficacy improvement despite higher density, p > 0.05). Microscopic measurements confirmed that nano-pesticides exhibited excellent wetting dynamics, reducing the final contact angle on hydrophobic maize leaves to as low as 28.24° (achieving a maximum dynamic reduction rate of 54.48%), significantly enhancing interfacial adhesion. This mechanism compensated for low-volume spraying limitations, allowing a 30% dosage reduction to maintain a robust field efficacy of 90.6%, whereas a 50% reduction significantly compromised efficacy (<72.5%). Ultimately, this study established “22.5 L/ha + 200 μm + 30% reduction” as the optimal operational parameter combination, providing a solid theoretical foundation and crucial technological support for precision plant protection and “pesticide reduction with efficiency enhancement” in high-density maize cultivation.