Downwash airflow of spray UAVs in agriculture has been gradually focused on and a few studies have already been involved in it. Thus, this paper firstly reviewed the current research of downwash airflow of UAVs for agricultural spray. It is found that Chinese scholars have published the majority of the studies in this area. Furthermore, two aspects of this kind of research, one, the work related to the caused results of UAV downwash acting on spray, the other, that regarding downwash itself for investigation, were analysed. It is illustrated that the present research usually uses water-sensitive paper, electronic devices, like wind-speed arrays and PIV, and CFD simulation. However, the existed electronic devices have some limitations, such as lacking of the ability of showing and demonstration of downwash in big enough areas in detail during recording. Moreover, the studies directly related to downwash have been just launched around the world and the examination for multi-rotor UAVs is even seriously not enough. Therefore, in terms of research trends, it is significant to develop specialised sensors for downwash measurement. The general and reliable CFD models of a class of UAVs are also needed. In addition, the combination of UAV downwash with real applications and actual production is important for the clarification of the details in different situations. Keywords: spray UAV, downwash, representation results, investigation methods DOI: 10.33440/j.ijpaa.20190201.0023 Citation: Yang S H, Zheng Y J, Liu X X. Research status and trends of downwash airflow of spray UAVs in agriculture. Int J Precis Agric Aviat, 2019; 2(1): 1–8.

The classification of plug seedlings is important work in the replanting process. This paper proposed a classification method for plug seedlings based on transfer learning. Firstly, by extracting and graying the interest region of the original image acquired, a regional grayscale cumulative distribution curve is obtained. Calculating the number of peak points of the curve to identify the plug tray specification is then done. Secondly, the transfer learning method based on convolutional neural network is used to construct the classification model of plug seedlings. According to the growth characteristics of the seedlings, 2286 seedlings samples were collected to train the model at the two-leaf and one-heart stages. Finally, the image of the interest region is divided into cell images according to the specification of the plug tray, and the cell images are put into the classification model, thereby classifying the qualified seedling, the unqualified seedling and the lack of seedling. After testing, the identification method of the tray specification has an average accuracy of 100% for the three specifications (50 cells, 72 cells, 105 cells) of the 20-day and 25-day pepper seedlings. Seedling classification models based on the transfer learning method of four different convolutional neural networks (Alexnet, Inception-v3, Resnet-18, VGG16) are constructed and tested. The classification accuracy of the VGG16-based classification model is the best, which is 95.50%, the Alexnet-based classification model has the shortest training time, which is 6 min and 8 s. This research has certain theoretical reference significance for intelligent replanting classification work.

Air-assisted sprayers are widely employed in orchards, but inappropriate spray parameters can lead to large droplet losses, pesticide waste, and environmental pollution. To investigate the factors affecting the droplet loss of an air-assisted sprayer behind canopies, a two-factor, five-level full experiment was conducted in an actual orchard, where the two factors were the power gradient and foliage area volume density (FAVD). In addition, the location of the sampling point was also considered in the data analysis, including horizontal distance, forward distance, and height. The results show that all factors significantly affected droplet coverage (p-value < 0.01). The droplet coverage showed an increase and then a decrease with an increasing power gradient, and the maximum coverage was measured at power gradient P3 (forward speed: 0.49 m/s, spray pressure: 0.30 MPa, and spray flow rate: 7.13 L/min) or P4 (forward speed: 0.58 m/s, spray pressure: 0.35 MPa, and spray flow rate: 8.44 L/min). The effect of FAVD on droplet coverage had obvious regularity, and this regularity did not change with the power gradient. At different positions behind canopies, the droplet coverage had great differences. The droplet coverage gradually decreases with increasing horizontal distance and height, while increasing with forward distance. This study provides a reference for the air-assisted sprayers to reduce droplet loss, and data support for subsequent research on precision spraying based on FAVD.