In plant protection, the increasing maturity of unmanned aerial vehicle (UAV) technology has greatly increased efficiency. UAVs can adapt to multiple terrains and do not require specific take-off platforms. They do well, especially in farmland areas with rugged terrain. However, due to the complex flow field at the bottom of a UAV, some of the droplets will not reach the surface of a plant, which causes pesticide waste and environmental pollution. Droplet deposition is a good indicator of the utilization rate of pesticides; therefore, this review describes recent studies on droplet deposition for further method improvement. First, this review introduces the flight altitude, speed, and environmental factors that affect pesticide utilization efficiency and then summarizes methods to improve pesticide utilization efficiency from three aspects: nozzles, electrostatic sprays, and variable spray systems. We also point out the possible direction of algorithm development for a UAV’s precision spray.
Fast assessment of the initial carbon to nitrogen ratio (C/N) of organic fraction of municipal solid waste (OFMSW) is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process. In this study, a novel approach was proposed to estimate the C/N of OFMSW, where an instance segmentation model was applied to predict the masks for the waste images. Then, by combining the instance segmentation model with the depth-camera-based volume calculation algorithm, the volumes occupied by each type of waste were obtained, therefore the C/N could be estimated based on the properties of each type of waste. First, an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks (Mask R-CNN) model. Second, a volume measurement algorithm was proposed, where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property. Then the calculated volume was corrected with linear regression models. The results showed that the trained instance segmentation model performed well with average precision scores AP 50 = 82.9, AP 75 = 72.5, and mask intersection over unit (Mask IoU) = 45.1. A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R 2 =0.97 and root mean square error RMSE = 0.10. The relative average error was 0.42% and the maximum error was only 1.71%, which indicated this approach has potential for practical applications.
In order to improve the deposition and uniformity of the pesticide sprayed by the agricultural spraying drone, this study designed a novel spraying system, combining air-assisted spraying system with electrostatic technology. First, an air-assisted electrostatic centrifugal spray system was designed for agricultural spraying drones, including a shell, a diversion shell, and an electrostatic ring. Then, experiments were conducted to optimize the setting of the main parameters that affect the charge-to-mass ratio, and outdoor spraying experiments were carried out on the spraying effect of the air-assisted electrostatic centrifugal spray system. The results showed the optimum parameters were that the centrifugal rotation speed was 10 000 r/min, the spray pressure was 0.3 MPa, the fan rotation speed was 14 000 r/min, and the electrostatic generator voltage was 9 kV; The optimum charge-to-mass ratio of the spray system was 2.59 mC/kg. The average deposition density of droplets on the collecting platform was 366.1 particles/cm 2 on the upper layer, 345.1 particles/cm 2 on the middle layer, and 322.5 particles/cm 2 on the lower layer. Compared to the results of uncharged droplets on the upper, middle, and lower layers, the average deposition density was increased by 34.9%, 30.4%, and 30.2%, respectively, and the uniformity of the distribution of the droplets at different collection points was better.
Automatically identifying the degradability of municipal solid waste (MSW) is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes. In this study, a cost-effective method was proposed for the degradability identification of MSW. Firstly, the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site. Secondly, a lite convolutional neural network (CNN) model was built with only 3.37 million parameters, and then a total of eight models were trained on these datasets with and without the image augmentation operations, respectively. Finally, a degradability identification system was built for on-site composting, where the images were cut to different sizes of small squares for prediction, and the experiments were conducted to find the best combinations of the trained models and the cutting size. The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher, and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%, which indicated the capability of this cost-effective method to identify the degradability of MSW.
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