• Journal of the Korean Association of Geographic Information Studies
• /
• 제22권3호
• /
• pp.107-119
• /
• 2019

As the development of ICT and integration technology, many changes and innovations in agriculture field are implemented. The agricultural sector has shifted from a traditional industry to a new industrial form called the 6th industry combined with various advanced technologies such as ICT and IT. Various approaches have been attempted to analyze and predict crops based on spatial information. In particular, a variety of research has been carried out recently for crop cultivation and smart farms using drones. The goal of this study was to establish an agricultural drought monitoring system using drones to produce scientific and objective indicators of drought. A soil moisture sensor was installed in the drought area and checked the actual soil moisture. The soil moisture data was used by the reference value to compare and analyze the temperature and NDVI established by drones. The soil temperature by the drone thermal image sensor and the NDVI by the drone hyperspectral was analyzed the correlation between crop condition and soil moisture in study area. To verify this, the actual soil moisture was calculated using the soil moisture measurement sensor installed in the target area and compared with the drone performance. This study using drone drought monitoring system may enhance to promote the crop data and to save time and economy.

• Korean Journal of Remote Sensing
• /
• 제38권6_1호
• /
• pp.1489-1503
• /
• 2022

Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.

• Journal of Drive and Control
• /
• 제21권2호
• /
• pp.53-64
• /
• 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.

• Korean Journal of Remote Sensing
• /
• 제35권5_1호
• /
• pp.637-647
• /
• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• 제22권3호
• /
• pp.491-498
• /
• 2018

As unmanned aerial vehicle(UVA), drone means a flying object that could aviate the route entered by a program in advance or remotely-controlled when a pilot is not on board. Drone that has been initially developed for the military purpose is currently used for diverse areas such as agricultural industry, leisure activity, logistics service, and life-saving area. Out of these areas, the shooting drone equipped with a camera is actively used for diverse image contents production areas including film and broadcasting area. This paper examines the characteristics of drone for the purpose of shooting, and also handles the shooting techniques using drone. Especially, this study aims to suggest and discuss the methods to shoot diverse camera working used by the existing image shooting with the use of drone after examining the operation of shooting drone used for the image contents production area.

• The Journal of the Convergence on Culture Technology
• /
• 제10권2호
• /
• pp.531-536
• /
• 2024

Control drones, which are recently classified as smart agricultural machines in the agricultural field, are striving to build smart control and automatic control systems by combining hardware and software in order to shorten working hours and increase the effectiveness of control in the aging era of rural areas. In this paper, the characteristics of the nozzle dedicated to the control drone were analyzed as a basic study for the establishment of management control and automatic control systems. In order to consider various variables such as the type of various drone models, controller, wind, flight speed, flight altitude, weather conditions, and UAV pesticide types, related studies are needed to be able to present the drug spraying criteria in consideration of the characteristics and versatility of the nozzle. Therefore, to enable the consideration of various variables, flow analysis (CFD) simulation was conducted based on the self-designed nozzle, and the theoretical and experimental values of the droplet distribution were compared and analyzed through water reduction experiments. In the future, we intend to calculate accurate scattering in consideration of various variables according to drone operation and use it in management control and automatic control systems.

• Korean Journal of Remote Sensing
• /
• 제38권6_1호
• /
• pp.1231-1244
• /
• 2022

The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.

• Korean Journal of Remote Sensing
• /
• 제40권3호
• /
• pp.257-268
• /
• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of the Institute of Convergence Signal Processing
• /
• 제25권1호
• /
• pp.27-32
• /
• 2024

Recently, in the era of the 4th industry, the era of smart agriculture is progressing with the use of related core technologies in the agricultural sector. As a representative example, the use of drones for pest control is increasing, and the use in the agricultural sector is increasing, and the existing control method is being changed by replacing the aging population. However, the importance of control management is increasing due to the increase in agricultural control drones. In this study, various civil complaints are occurring due to the non-standardization of the control operator's work instructions, control area allocation, and control settlement. In this study, we try to resolve civil complaints by computerizing various tasks that occur from the drone control manager's point of view and computerizing them so that they can be managed. Through this, it is intended to manage the control area for large areas and use it as basic data for the development of control management system.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 한국작물학회 2022년도 추계학술대회
• /
• pp.129-129
• /
• 2022

The production area of Italian ryegrass feed is gradually increasing and labor-saving technologies are being developed. If sowing and fertilization are carried out agricultural drones, working hours and labor are reduced. The purpose of this study is to suggest an appropriate seedling amount for feed production by drone spreading of Italian ryegrass in spring. In addition, we would like to review the productivity of the drone seeding machine that is being developed by Rural Development Administration(RDA) of Korea. Italian ryegrass was sown by a drone in February at the NICS located in Gyehwa-hwa, Jeollabuk-do, South Korea. In Experiment 1, 50kg/ha, 60, 70, and 80 seeding rates were sown with a horizontal spreader drone. In Experiment 2, uniform spreaders type drone and horizontal spreader type were sown with the same seeding amount and compared. The drone was sown using the AF-52 aircraft. The higher the seeding amount, the higher the emergence rate. As the seeding amount increased, the plant length increased, but the number of tillers per individual decreased. The dry matter weight of the feed was the highest at 1,326kg/10a at the seeding rate of 70kg/ha, and decreased by 20.5% at the seeding rate of 80kg/ha. The coverage ratio was the highest at 96 at the seeding rate of 70kg/ha, which was the most advantageous for spring sowing. In the comparative experiment according to the spreader type, the uniform spreader had a high emergence rate per unit area. When the uniform spreader was used, the dry matter weight of the feed was 17% higher than that of the horizontal one, and the coverage was about 5% higher.