• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.135-142
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• 2020

In the recent farming industry, there is a growing diffusion of drones, which are recognized as a crucial technology of the 4 th industrial revolution to cope with aging. Especially, filming and pest control using drones are representative fields that have different age groups for obtaining a national license of multicopter that is a ultra-light flying device, and can create profits after getting a license. However, pest control technology using drones has different spray effects depending on levels of operational proficiency, since this highly relies on an operator's operating skills. It is anticipated that if this issue is supplemented, the use of drones for pest control in the farming industry will diversify. For analysis of spraying characteristics of agricultural pest control drones, this study aims to formulate effective spraying hours and effective spraying intervals and suggest an algorithm, which facilitates an accurate calculation of pest control area depending on the kinds of pest control drones. This algorithm can be used in the field of pest control by improving scatterling issues caused by drone flight methods of drone pest controllers and building an optimum pest control manual in future.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.108-113
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• 2019

In relation to $4^{th}$ industrial revolution, it is required to build a smart agricultural system using the pest control drones, which are emerging fast these days as a role to support pest control work of farmers and improve aging issues in farming. However, the absence of accurate criteria on management of the pest control drones and the effect of pesticide application is leading to damage to crops by pesticides. The extreme shortage of analysis of management of the pest control drones and relevant studies, and big differences in pest control efficiency depending on the operation skills of controllers are the biggest reasons for the damage. Therefore, this paper suggests a basic study on agricultural pest control drone operation system buildup to make out working schedules and calculate the dosage of pesticide by understanding the features of the pest control drones properly based on the control using smart operating mode.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.569-576
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• 2023

An agricultural drones are gradually increasing in utilization due to economic efficiency, and consist of a main frame in charge of flying spray system in charge of moving pesticide to control targets. Therefore, the environment and characteristics of crops should be considered when controlling pesticides using drones and conditions such as systematic flying altitude of flight, speed, and spray time should be changed accordingly. However, pest control work using agricultural drones has different spray effects depending on level the operation proficiency and spray impact. In addition, there are variations in operating standards and control efficiency for agricultural drones, which hinder the distribution of agricultural control drones in the field of pest control work. Therefore, this study attempts to identify the spraying characteristics of agricultural drones, apply the effective spraying time, interval and experimentally verify the system that can calculation of spray area compared to previous studies. Through this experimental verification, it is intended to apply the optimal control process by minimizing the obstacles to pest control work by applying the operation method and systematic figures to agricultural drones.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Journal of Drive and Control
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• v.20 no.3
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• pp.25-34
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• 2023

In this study, we conducted a research on optimizing the spraying performance of agricultural drones and predicted the spraying performance in various flight conditions using the multi-layer perceptron (MLP). Data was collected using a test device for pesticide spraying performance according to the water sensitive paper (WSP) evaluation. MLP training involved supervised learning to achieve a coefficient of variation (CV), which indicates the degree of uniform spraying. The performance evaluation was conducted using R-squared (R²), the test samples showed an R² of 0.80. The results of this study showed that drone spraying performance can be predicted under various flight environments. In addition, the correlation analysis between flight conditions and predicted spraying performance will be useful for further research on optimizing the spraying performance of agricultural drones.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1049-1055
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• 2023

The utilization of agricultural drones for pest control operations has been increasing due to its economic efficiency. However, variations in the effectiveness of these operations occur depending on the operator's proficiency. In this study, we applied a smart operating mode to overcome the limitations of manual flight mode and proposed a numerical model. Through comparative validation with prior research, we conducted experimental verification. As a result, we determined the spray time and calculation of spray area for each drone model. We selected a drone for pest control with a high similarity to the numerical model and verified it experimentally. Through this, we confirmed that the application of the smart operating mode is more effective in terms of calculation of spray area and operational efficiency compared to manual flight mode.

• Korean journal of applied entomology
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• v.57 no.4
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• pp.347-354
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• 2018

In order to setting the control standard of Chinese cabbage pests using a drone, the downward wind speed, spraying width, and the number of falling particles and particle size were examined using a water sensitive paper with spray different heights (3, 4, 5 m) and flying speeds (3, 4 m/sec). Fore kinds of pesticides for aviation control were used to test the perfect lethal concentration and dose for major pests of Chinese cabbage such as Plutella xylostella, Spodoptera exigua and Spodoptera litura. The number of falling particles in spraying pesticides with drones was 80.5% on the upper side, 14.8% on the vertical side, and 4.7% on the back side. The number of falling particles as different spray heights were 3 m = 53, 4 m = 40 and $5m=39particles\;cm^{-2}$. The number of falling particles as different flying speeds were $3m\;sec^{-1}=62$ and $4m\;sec^{-1}=25particles\;cm^{-2}$. In the laboratory test, the perfect lethal concentration and dose of Plutella xylostella was chlorfenapyr SC (20 times, $0.5{\mu}l$) and bistrifluron chlorfenapyr SC (25 times, $0.5{\mu}l$). The perfect lethal concentration and dose of Spodoptera exigua was chlorfenapyr SC (20 times, $1{\mu}l$), bistrifluron chlorfenapyr SC (20 times, $1{\mu}l$), and chlorfenapyr SC (20 times, $1{\mu}l$) and bistrifluron chlorfenapyr SC (20 times, $0.5{\mu}l$) for Spodoptera litura. Therefore, the main pest control method of Chinese cabbage using drones is 20 times diluted chlorphenapyr SC or bistrifluoruron-chlorphenapyr SC, sprayed at 3 m height by $3msec^{-1}$ of going speed. This spraying method will be effective for control of Chinese cabbage pest.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.637-648
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• 2021

In order to support the establishment of a farming plan, it is important to preemptively evaluate crop changes and to provide precise information. Therefore, it is necessary to provide customized information suitable for decision-making by farming stage through scientific and continuous monitoring using drones. This study was carried out to support the establishment of the farming plan for ground vegetable. The cultivation management map of each information was obtained from preliminary study. Three cultivation management maps include 'field emergence map', 'stress map' and 'productivity map' reflected spatial variation in the plantation by providing information in units of plants based on 3-dimensions. Application fields of the cultivation management map can be summarized as follows: detect miss-planted, replanting decision, fertilization, weeding, pest control, irrigation schedule, market quality evaluation, harvest schedule, etc.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.