Plant protection drone spraying technology is widely used to prevent and control crop diseases and pests due to its advantages of being unaffected by crop growth patterns and terrain restrictions, high operational efficiency, and low labor requirements. The operational parameters of plant protection drones significantly impact the distribution of spray droplets, thereby affecting pesticide utilization. In this study, a field experiment was conducted to determine the working modes of two representative plant protection drones and an electric backpack sprayer as a control to explore the characteristics of droplet deposition with different spray volumes in the citrus canopy. The results showed that the spraying volume significantly affected the number of droplets and the spray coverage. The number of droplets and the spray coverage area on the leaf surface were significantly increased by increasing the spray volume from 60 L/ha to 120 L/ha in plant protection drones. Particularly for the DJI T30, the mid-lower canopy showed a spray coverage increase of 52.5%. The droplet density demonstrated the most significant variations in the lower inner canopy, ranging from 18.7 droplets/cm² to 41.7 droplets/cm² by XAG V40. From the deposition distribution on fruit trees, the plant protection drones exhibit good penetration ability, as the droplets can achieve a relatively even distribution in different canopy layers of citrus trees. The droplet distribution uniformity inside the canopy is similar for XAG V40 and DJI T30, with a variation coefficient of approximately 50%-100%. Compared to the plant protection drones, the knapsack electric sprayer is suitable for pest and disease control in the mid-lower canopy, but they face challenges of insufficient deposition capability in the upper canopy and overall poor spray uniformity. The distribution of deposition determined in this study provides data support for the selection of spraying agents for fruit trees by plant protection drones and for the control of different pests and diseases.

Tryptophan, as a signal molecule, mediates many biotic and environmental stress-induced physiological responses in plants. Therefore, an experiment was conducted to evaluate the effect of tryptophan seed treatment in response to cadmium stress (0, 0.15, and 0.25 mM) in sunflower plants. Different growth and biochemical parameters were determined to compare the efficiency of the treatment agent. The results showed that cadmium stress reduced the growth attributes, including root and shoot length, dry and fresh weight, rate of seed germination, and the number of leaves. Cadmium stress also significantly reduced the contents of chlorophyll a, b, and total chlorophyll, carotenoid contents, phenolics, flavonoids, anthocyanin, and ascorbic acid. Whereas cadmium stress (0.15 and 0.25 mM) enhanced the concentrations of malondialdehyde (45.24% and 53.06%), hydrogen peroxide (−11.07% and 5.86%), and soluble sugars (28.05% and 50.34%) compared to the control. Tryptophan treatment decreased the effect of Cd stress by minimizing lipid peroxidation. Seed treatment with tryptophan under cadmium stress improved the root (19.40%) and shoot length (38.14%), root (41.90%) and shoot fresh weight (13.58%), seed germination ability (13.79%), average leaf area (24.07%), chlorophyll b (51.35%), total chlorophyll (20.04%), carotenoids (43.37%), total phenolic (1.47%), flavonoids (19.02%), anthocyanin (26.57%), ascorbic acid (4%), and total soluble proteins (12.32%) compared with control conditions. Overall, the tryptophan seed treatment showed positive effects on sunflower plants’ growth and stress tolerance, highlighting its potential as a sustainable approach to improve crop performance.

Brassinolides (BRs) are naturally-occurring phytohormones, which are essentially important to improve the crop adoptive capacity to various stresses. Spray volume (SV) and agrochemical application methods are associated with chemical deposition and field efficiency. The objective of this study was to compare the possible effects of 28-Homobrassinolide (HBL) dosages 18, 22, and 30 mg a.i. ha⁻¹ for unmanned aerial vehicle (UAV) sprayers (15 L ha⁻¹ and 30 L ha⁻¹) and 22 mg a.i. ha⁻¹ for Knapsack manual sprayers (KMS) (450 L ha⁻¹) at maize silking stage on droplets deposition distribution, photosynthetic parameters, grain filling process and yield. The results showed that the droplet deposition of UAV (15, 30 L ha⁻¹) was 47.04%, 8.89% higher than KMS. However, the UAV sprayer had a poor droplet deposition distribution. HBL significantly increased the photosynthetic parameters, grain filling rate, and yield. A UAV spray volume of 15 L ha⁻¹ with 22 mg a.i. ha⁻¹ significantly increased grains yield by 4.16–5.64%, 7.5–12.09% compared to KMS and CK in both years. Considering the high efficiency of the UAV sprayer and better effects of HBL on final yield, spraying 22~30 mg a.i. ha⁻¹ with UAV spray volume 15 L ha⁻¹ at the silking stage could be a better strategy.

The field environment is complex and variable, and multiple factors constrain the effectiveness of UAV applications, and a single flow rate application can no longer meet the requirements. Therefore, it is crucial to study a decision-making model of spraying flow for plant protection UAVs under multi-factor interaction. In this paper, based on a large amount of experimental data combined with Pearson correlation analysis and random forest variable importance score ranking, the experimentally acquired features are screened and input into the GA-BP neural network. The model evaluation results showed that the GA-BP neural network model has a correlation coefficient of 0.99 between the true value, predicted value, and a coefficient of determination of 0.98, which is better than the general regression model. A validation test was conducted to test the effectiveness of the model for new data. The final result yields an error value within ±20% for the GA-BP model to predict the flow rate. At the same time, the BP neural network fluctuated more for some of the predicted values, which caused a 50% increased error in fitting results. It proves the feasibility of the BP neural network optimized based on a genetic algorithm in plant protection UAV flow rate decision-making, which can provide a reference basis and scientific guidance for precise variable spraying operation of plant protection UAVs.