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BACKGROUND: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground‐based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling. RESULTS: Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling. CONCLUSION: UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Presents the fuzzy neural network for fire detection. Calculation of model of fuzzy inference to process fire signals; Application of fuzzy operations to analyze different kind of signals; Offer of signal processing method for fire detection with fuzzy and adaptive characteristics.

Considering the difficulty of broadcasting in the small plots and complex terrain in South China, this research aimed to explore a new efficient broadcasting way and figure out advisable operation parameters by using a hollow 12-axis, rotor-wing Unmanned Aerial Vehicle (UAV) which is typically made up of four groups of solid support structure, with each consisting of three axes and two rotor wings. A 3.7 L reverse pyramid-shape seed hopper with a 60 mm×13 mm rectangular outlet at the bottom was designed to realize self-gravity seeding. Rice seed firstly directly falls on the rotating disc driven by direct-current dynamo before being sown. The disc was located 120 mm above the ground, with a diameter of 350 mm. Under constant flight conditions, parameters of the on-board broadcasting devices (dropping speed referring to speed for the outlet and the broadcasting speed for the disc's rotation speed) determine the uniformity of air broadcasting. The FUTABA T8FG transmitter and receiver system were employed as the remote control device. When the UAV flies at 3 m/s and 2 m above the ground with an expected seeding of 180 grain/m2, the RD (Right Down) knob marked 29 and LD (Left Down) knob marked -24 with a disc rotation speed of 900 r/min, the broadcasting lasts for 15 seconds with a 25% opening (1.95 cm2) of rectangular outlet. In order to verify the feasibility of air broadcasting parameters, the project team carried out a field broadcasting test by using a hollow multi-rotor UAV at Zhongluotan Test Base in Guangzhou in July, 2014. In the square field plots of 0.09 hm2, flying path of air broadcasting operation was designed. The collected sampling data of broadcasting quantity per unit area after the test showed that there were, on average, 187.4 grains/m2 of the five sampling points with a standard deviation of 22.77, and a coefficient of variation of 12.15% which was far smaller than that of artificial broadcasting. The average yield of field broadcasted by UAV is 7705.5 kg/hm2 in 2014, implying that rice air broadcasting by UAV is feasible. [ABSTRACT FROM AUTHOR]

Plant protection unmanned aerial vehicles (UAVs) have become popular in mountain orchards, but due to the differences in planting structures, the chances of heavy spraying, missed spraying and pesticide drift are increasing. To mitigate the adverse effects of these phenomena, it is necessary to clarify the effective deposition range of aerial spray droplets. This study proposed an effective spray swath determination method for the effective spraying range of mountainous orchards with UAVs equipped with a mist nozzle (bilateral 1% coverage). This approach focused on exploring the effects of flight height (unidirectional flight modes of 2, 3 and 4 m), spray nozzle atomization performance (reciprocating flight modes of 20, 30 and 40 µm) and flight route (treetop flying and inter-row flying) on the spraying range in a mountain setting. In addition, the study analysed the relationship between the droplet-size spectrum and the effective swath position. The results showed that it is feasible to use the bilateral 1% coverage evaluation method to determine the effective spray swath of a UAV adapted with a mist nozzle for aerial operation in a mountainous Nangguo Pear orchard. With the increase in UAV flight height (2-4 m), the effective unidirectional spray swath also increased, and with the increase in atomization level (20-40 μm), the effective reciprocating spray swath showed a decreasing trend. Moreover, the average effective swath width measured by the UAV for treetop flight was greater than that measured for inter-row flight. The study also found that the proportion of small droplets (droplet size less than 100 µm) below the UAV route was lower (approximately 50%) than along the sides of the route (approximately 80%), and the spray swath was not symmetrically distributed along the flight route but shifted laterally by approximately 3 to 4 m in the downhill direction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Yao, Guo, Yan, Yu, Meng, Chen and Chen.)