Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.

Rotary atomizers are mainly used in agricultural manned aircrafts. Atomization characteristics at high speeds have been studied, but methods to measure the atomization efficiency have not been elucidated. The atomization efficiency of rotary atomizers under high-speed airflow was investigated using an IEA-I high-speed wind tunnel experimental installation, AU5000 rotary atomizer, and a laser diffraction particle size analyzer. Accordingly, a model equation for atomization efficiency measurements was innovatively obtained. When the flow rate, fan blade angle of the atomizer, and wind speed were used as variables, the experimental results showed that the atomization efficiency mainly depended on the fan blade angle. When the fan blade angle was 35°, the atomization efficiency was optimal, regardless of wind speed. In contrast, when the fan blade angle of the atomizer was 65°, it exhibited the worst atomization efficiency, regardless of the wind speed. The experimental data from this study can provide guidance for aerial application in fixed-wing manned aircraft, such as the flow rate, and operating speed.

The development of unmanned aerial spraying systems (UASSs) has significantly transformed pest and disease control methods of crop plants. Precisely adjusting pesticide application rates based on the target conditions is an effective method to improve pesticide use efficiency. In orchard spraying, the structural characteristics of the canopy are crucial for guiding the pesticide application system to adjust spraying parameters. This study selected mango trees as the research sample and evaluated the differences between UAV aerial photography with a Structure from Motion (SfM) algorithm and airborne LiDAR in the results of extracting canopy parameters. The maximum canopy height, canopy projection area, and canopy volume parameters were extracted from the canopy height model of SfM (${CHM}_{SfM}$) and the canopy height model of LiDAR (${CHM}_{LiDAR}$) by grids with the same width as the planting rows (5.0 m) and 14 different heights (0.2 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, 0.8 m, 1.0 m, 2.0 m, 3.0 m, 4.0 m, 5.0 m, 6.0 m, 8.0 m, and 10.0 m), respectively. Linear regression equations were used to fit the canopy parameters obtained from different sensors. The correlation was evaluated using $R^2$ and $rRMSE$, and a t-test (α = 0.05) was employed to assess the significance of the differences. The results show that as the grid height increases, the $R^2$ values for the maximum canopy height, projection area, and canopy volume extracted from ${CHM}_{SfM}$ and ${CHM}_{LiDAR}$ increase, while the $rRMSE$ values decrease. When the grid height is 10.0 m, the $R^2$ for the maximum canopy height extracted from the two models is $92.85\%$, with an $rRMSE$ of $0.0563$. For the canopy projection area, the $R^2$ is 97.83%, with an $rRMSE$ of 0.01, and for the canopy volume, the $R^2$ is 98.35%, with an $rRMSE$ of 0.0337. When the grid height exceeds 1.0 m, the t-test results for the three parameters are all greater than 0.05, accepting the hypothesis that there is no significant difference in the canopy parameters obtained by the two sensors. Additionally, using the coordinates $x_0$ of the intersection of the linear regression equation and $y=x$ as a reference, ${CHM}_{SfM}$ tends to overestimate lower canopy maximum height and projection area, and underestimate higher canopy maximum height and projection area compared to ${CHM}_{LiDAR}$. This to some extent reflects that the surface of ${CHM}_{SfM}$ is smoother. This study demonstrates the effectiveness of extracting canopy parameters to guide UASS systems for variable-rate spraying based on UAV oblique photography combined with the SfM algorithm.

The spatial distribution of droplet size and velocity affects the deposition and distribution on the target. In order to investigate the influence of different adjuvant and pressures on the spatial distribution of droplet size and velocity in atomization area of different nozzles, air induction flat fan nozzle IDK120-03, multi-range flat fan nozzle LU120-03 and anti-drift flat fan nozzle AD120-03 were selected. Phase Doppler Interferometer (PDI) was used to analyze and compare the distribution of droplet size and velocity in the atomization area of three nozzles when four typical adjuvant Maisi, Maidao, Adsee AB-600 and Surun sprayed at different pressures. The results show that the volume median diameter of droplet size has no obvious change along the vertical direction of the nozzle center and increases with distance in the horizontal direction, the droplet size decreases with increasing pressure at the same position, the adjuvant all increases the droplet size (about 12%, 12%, 10% and 9% for Maisi, Maidao, Surun and Adsee AB-600, respectively), IDK120-03 nozzle droplet size is the largest and LU120-03 nozzle is the smallest in the same position. For droplet velocity distribution, droplet velocity decrease in distance along the vertical and horizontal direction, respectively, the droplet velocity increases with increasing pressure at the same position, compared with water, the droplet velocity increased by about 13%, 9%, 8%, and 4% for Maisi, Maidao, Surun, and Adsee AB-600, respectively, the velocity of AD nozzle is the largest and IDK nozzle is the smallest at the same position. The experiment can provide a basis for the selection of adjuvants and nozzles in pesticide application, and provide a data base for studying the distribution of droplets on the target.

During the chemical application process, droplet deposition on a target is an important reference indicator for evaluating the spraying technique and its performance. In order to quickly obtain deposition results in the field, this study proposed a novel system based on surface humidity sensors. The basic principle is to convert the measured physical quantity change into a capacitance change, thereby realizing the physical quantity to electrical signal conversion. An Android application for mobile terminal and corresponding coordinator were developed, which allowed operators to control multiple sensors simultaneously through the Bluetooth. The soluble tracer detected by spectrophotometer was used to calibrate the system. The obtained results indicated a good correlation between deposition volume and voltage increment output from the newly system (R2 of the six nozzles with Dv0.5 ranging from 107.28 μm to 396.20 μm were 0.8674-0.9729), and a power regression model based on the least squares technique (R2=0.8022) was developed. In the field test, the system exhibited an optimal performance in predicting the deposition volume. Compared with the conventional method of measuring tracer concentration, the deviation was less than 10%. In addition, the system exhibited good fitting curve of the deposition distribution with droplet number results measured by the water sensitive paper method. Keywords: deposition measurement, agricultural sprayer, droplets, intelligent sensor DOI: 10.25165/j.ijabe.20211405.5528 Citation: Li L L, Zhang R R, Chen L P, Yi T C, Xu G, Xue D X, et al. Development of sensor system for real-time measurement of droplet deposition of agricultural sprayers. Int J Agric & Biol Eng, 2021; 14(5): 19–26.

The structural parameters of the liquid sheet represent a significant factor influencing the atomization performance, and its measurement is an important part of the agrochemical atomization study. Currently, the measurement predominantly relies on commercial software with manual operation, which is labor intensive and inefficient. In this study, deep learning methods with high-speed photographing were employed to measure the structural parameters of the liquid sheet of hydraulic nozzles with different atomization modes. The LM-YOLO liquid sheet structure recognition model was constructed to recognize the liquid sheet and perforations. Based on the recognition results, a method is designed to calculate several key parameters, including the breakup length, the liquid sheet area, the spray angle, the average number of perforations, and the average perforation area. A comparative scrutiny of the assorted liquid sheet structural parameters under different experimental conditions was also implemented. Based on the constructed model, a recognition accuracy of 81.0% for the liquid sheet structure of the LU nozzle (a classical hydraulic nozzle with high liquid sheet integrity) and 71.3% for the IDK nozzle (an air-induced hydraulic nozzle with a certain amount of bubbles in the liquid sheet) was achieved. The liquid sheet structure was measured based on the recognition results. It was found that the pressure has a significant impact on the structural parameters of the liquid film. For the LU120-03 nozzle, the breakup length of the liquid film decreases from 48.96 mm to 39.05 mm as the pressure increases. In contrast, for the IDK120-03 nozzle, the breakup length exhibits fluctuating changes, with a peak value of 29.65 mm occurring at 250 kPa. After adding silicone adjuvant, the breakup length and area of the liquid film generally decrease. The variation trends of the measured structural parameters under different experimental conditions are consistent with the trends of the data in previous relevant research by other scholars. This study provides a new method for measuring out the structural parameters of the liquid sheet, and it has potential application in related fields.