• Korean journal of applied entomology
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• v.58 no.4
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• pp.271-280
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• 2019

We investigated the control efficacy and phytotoxicity of unmanned aerial vehicle-applied pesticides against the insect Spodoptera exigua, a major emerging pest in Chinese cabbage. Phytotoxicity was investigated in cabbage and 7 crops cultivated in the perconducted on 8 surrounding crops including Chinese cabbage at 1 to 2 times the recommended pesticide dosage. We treated cabbage fields with spinetoram suspension concentrate (16×), methoxyfenozide, sulfoxaflor suspension concentrate (16×). Then, we used water-sensitive paper to measure the distribution pattern of falling pesticide particles and the degree of coverage. Two of the pesticides showed 97% control efficacy, however, control efficacy might differ in resistant populations. Phytotoxicity was not observed in Chinese cabbage and the 7 surrounding crops treated with 1 to 2 times the recommended pesticide dosage. Analysis of the distribution pattern of falling pesticide particles revealed that breeze caused particle diffusion. Thus, wind is an important factor affecting the uniform treatment and diffusion of multicopter-applied pesticides. It follows that setting optimal conditions is necessary for effective control and treatment.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.423-433
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• 2023

As aerial application increasing, with social concerning in pesticide drift rises, so this study attempts to establish a test bench that can repeatedly and continuously evaluate this. To this end, this study first analyze ISO 22866 and ASABE S561.1 among the international standard test methods related to pesticide fugitive evaluation. A test bench was established at the Naju practice field of Chonnam National University in accordance with international standards, and field tests were carried out (ISO 22866, ASABE S561.1) to verify effectiveness. A test bench that established in this study and a pesticide drift recovery protocol by aerial application can improve the experimental environment where field experiments were complex and it was difficult to achieve the same conditions. In addition, it will be possible to construct a database of pesticide drift that takes into account various factors that affect pesticide drift substances, which is expected to improve the reliability of the data, as well as quantitative evaluation of pesticide drift in the air.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.71-91
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• 2018

In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of $7.5{\mu}m$ to $13.5{\mu}m$ was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.

• Journal of Biosystems Engineering
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• v.31 no.4 s.117
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• pp.342-348
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• 2006

Aerial application using an unmanned agricultural helicopter became necessary for both labor saving and timely spraying. In the previous paper, a rotor system was developed and lift capability was evaluated. The experimental results were compared with simulated predictions using the CFD-ACE program. From the simulation, the relative velocity on the top surface of the blade airfoil increased, resulting in the pressure drop. The CFD analyses were revealed that a drag resistance on the leading edge of the airfoil, a wake at the trailing edge, and a positive pressure underneath the bottom surface were observed. As the results of the simulation, total lifts of 56.8, 74.4 and $95.0kg_f$ were obtained at the 6, 8 and $10^{\circ}$ of AAT (angle of attack), respectively. The simulation results agreed reasonably up to $10^{\circ}$ of AAT. However, at a greater AAT $(<12^{\circ})$ the simulated total lift continuously increased to $105kg_f$, comparing with a decreasing experimental total lift due to the lack of engine power. At a stiff angle of $18^{\circ}$ AAT, a wake was observed at the trailing edge of the airfoil. A rated operating condition determined from the previous paper was also verified through the simulation.

• Korean Journal of Weed Science
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• v.30 no.4
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• pp.445-453
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• 2010

This study was conducted to investigate herbicidal efficacy and diffusibility of halosulfuron-methyl+mefenacet in water treated with 500 g great granule (GG) and 3 kg granule (GR). The 500 g GG was been spreading on the surface of the water within 6 minutes, 53 seconds, and it's active ingredient was diffused in the water bath of $10m^2$ size between 30 and 60 minutes. In addition, the diffusion of 500 g GG was influenced by moisture contents, so it have to immediately use 500 g GG in paddy fields when it was been unpacked. The herbicidal efficacy of the 500 g GG and 3 kg GR of halosulfuron-methyl+mefenacet was excellent to most weed species, but showed different efficacy for the control of Aneilema keisak and Scirpus juncoides, that was may be distribution pattern of active ingredient as different formulation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.