Background: Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. Results: Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. Conclusions: This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.

Target and off-target spray depositions determine the spray’s effectiveness and impact on the environment. A decisive stage in the measurement of spray deposition and drift is selecting an appropriate sampling approach under field conditions. There are various approaches available for sampling spray deposition and drift, during the evaluation of ground sprayers used for the UAV sprayer assessment, under field conditions. In this study, two sampling approaches (water-sensitive paper, and glass strip collectors) were compared to analyze spray deposition in target and off-target zones. The results showed a variation in the estimation of the spray deposits among the two applied sampling methods. The results showed that the water-sensitive paper recorded the droplet deposition in the target zone with a range from 0.049 to 4.866 µLcm⁻², whereas the glass strip recorded from 0.11 to 0.793 µLcm⁻². The results also showed the water sensitive paper recorded an 80.3% higher deposition than that of the glass strip at zero position during the driving flight height 2 m and flight speed 2 ms⁻¹ (T1 treatment). It can be concluded that variation in recorded depositing is due to the sampling material. It is recommended that the confident deposition results, measurement methods and sampling approaches must be standardized for UAV sprayers according to the field conditions and controlled within artificial assessments.

Background: The interaction between canopy and droplets is very important in the process of crop spraying. During the actual air-assisted application process, air-mist flow inevitably disturbs the leaves before droplets reaching them, which will also affect the final deposition state of the droplets on the leaf. Currently, researches on the interaction between droplets and the target leaf surface mainly focuses on the deposition behaviour on the surface of stationary target leaves rather than the dynamic leaves. Therefore, the deposition characteristics after the collision between the droplets and dynamic leaves are important for practical application and worth further study. Results: Computational fluid dynamics simulations were performed to characterise the surface roughness, contact angle, and mechanical vibration. The interaction platform between the droplet and the vibrating pear leaf was built for experimental verification under laboratory conditions. The simulation results are in good agreement with the experimental results, which revealed the main reason for the droplet spreading and sliding was the inertial force generated by the relative velocity. It also indicated that the pear leaf vibration can improve the deposition of low-velocity and small droplets, which is different from that of static pear leaves. Conclusion: The deposition effect of droplets in vibrating pear leaves was investigated. This study also provides a simulation method for the collision between a vibrating leaf and moving droplets, and provides reference for the study of droplet deposition characteristics under the vibration of fruit trees.

The suitability of airflow velocity in airborne spraying operations in orchards is mostly evaluated on the basis of inlet and outlet based on the airflow velocity at the canopy. However, the airflow velocity required to penetrate into the inner layer of the canopy, which is prone to pests and diseases, is still unclear due to variation in the geometry of the plant canopies. In this study, pear trees were selected as an example to explore the variations in the law of airflow attenuation in the inner canopy. Furthermore, we examine mist droplet formation in the inner canopy to determine a suitable inner canopy airflow end velocity (ICAEV) for air-assisted application. We also conducted a field validation test. The results showed that the majority of airflow velocity loss occurred in the middle and outer part of the canopy; rapid decline of airflow occurred in the 0–0.3 m section, whereas the slow decline of airflow occurred in the 0.3–0.8 m section. When the ICAEV is in the range of 2.70–3.18 m/s, the spraying effect is better. The droplet deposition variation coefficient was 42.25% compared with 51.25% in the conventional airflow delivery mode. Additionally, the droplet drift was reduced by 12.59 μg/cm². The results of this study can identify a suitable ICAEV for air-assisted spraying in orchards.

In the Huang-Huai-Hai region of China, the instability of electric boom sprayers has prompted many farmers to raise the boom height to improve clearance. However, the drift risks associated with these conditions remain poorly assessed. This study investigated two key factors influencing drift: boom height and nozzle type. The standard LI CHENG VP11003 nozzle was compared to the Teejet XR11003 nozzle, and droplet size and velocity were measured at various boom heights. The results showed that, at the same boom height, the LI CHENG nozzle produced droplets with an average D[V, 0.5] 14.6 µm larger (8.13%), an average velocity 0.53 m/s lower (29.26%), and a relative span (RS) value 0.05 higher (4.52%) compared to the Teejet nozzle. Drift tests were performed under field conditions using a spray drift test bench. The results showed that the total drift amount per unit area (TDA) for the LI CHENG nozzle showed minimal variation at boom heights of 0.4–0.6 m (Stage 1), 0.7–0.9 m (Stage 2), and 1.0–1.2 m (Stage 3). The drift potential of the LI CHENG VP11003 nozzle increased by 136.62% in Stage 2 and 282.69% in Stage 3, relative to Stage 1. Similarly, the Teejet XR11003 nozzle showed increases of 30.52% and 165.51% in Stages 2 and 3, respectively. The results showed that the LICHENG nozzle, which is the standard equipment on the sprayer, can only be used to moderately increase the boom height to improve the sprayer’s clearance within the range of the first stage. When the boom height exceeds this range, the drift risk becomes too high. This study provides meaningful insights into enhancing drift control and developing application strategies for growers.