Applicability Evaluation of Agricultural Subsidies Inspection Using Unmanned Aerial Vehicle

Park, Jin Ki; Park, Jong Hwa

doi:10.5389/ksae.2016.58.5.029

Cited by 8 publications

(8 citation statements)

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“…For each photograph, the latitude (x), longitude (y), and altitude (z), and the pitch (ω), roll (ψ), and yaw (χ) metadata of the drone were automatically saved into a log file. Each set of metadata was transferred into world coordinates and tracked using the key points of the photographs of nearby areas by applying a 3D rotation matrix [29] . The point cloud data with a 3D space were extracted from the registered images, and a DSM was produced in the form of a digital map using PhotoScan software (Ver.…”

Section: Digital Surface Model (Dsm)mentioning

confidence: 99%

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Jeon¹,

Choi

2020

International Journal of Agricultural and Biological Engineering

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Methods for producing high-resolution digital topographic maps using an unmanned aerial vehicle (UAV), and 3D fluid dynamics simulation to estimate the flooded areas caused by a collapsed reservoir, were proposed in this paper. The UAV flight path for photographing damaged areas was divided into two sections considering the drone flight time and overlapping range of the images in the x-and y-directions. The metadata taken by the drone were transferred into world coordinates by tracking the key features of the photographs of nearby areas using a 3D rotation matrix. The point cloud data with a 3D space were extracted from the registered images, and a digital surface map (DSM) was produced using a point cloud classification geometric mapping technique. To amend the serious elevation errors caused by natural or artificial obstacles, a kriging interpolation method was used to reproduce the DSM. A transient computational simulation that considers both the complex geometric topology and hydrodynamic energy of flowing water was conducted using FLOW-3D software to deal with an renormalization group (RNG) turbulence model. The flooded areas calculated through visual reading using images taken by the UAV were compared with the 3D simulation results for verification. The flooded areas estimated through the simulation were approximately 18.3% larger than those found by visual reading. Turbulent flows were mainly observed in obstacles or curved areas of the stream, and the differences in the water depth could be further increased. However, the villagers confirmed that the flooded areas were much greater than what was seen through the visual reading. Therefore, the combination of UAV surveying and the 3D simulation method based on the RNG turbulence model is recommended to accurately estimate flooded areas, and it will support an administrative policy aimed at minimizing the economic costs of damage caused by future reservoir collapses.

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Section: Digital Surface Model (Dsm)mentioning

confidence: 99%

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Jeon¹,

Choi

2020

International Journal of Agricultural and Biological Engineering

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“…Drone and sensor technologies are rapidly developing and being introduced into agricultural observation practices to increase their usability [25,26]. In agricultural observation, it is very important to accurately analyze the growth characteristics of each vegetable crop and to manage cultivation through the minimum input of manpower [27].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Growth Characteristics of Kimchi Cabbage Using Drone-Based Cabbage Surface Model Image

Lee

et al. 2022

Agriculture

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Cultivation soil is the basis for cabbage growth, and it is important to assess not only to provide information on how it affects the growth of vegetable crops but also for cultivation management. Until now, field cabbage surveys have measured size and growth variations in the field, and this method requires a lot of time and effort. Drones and sensors provide opportunities to accurately capture and utilize cabbage growth and variation data. This study aims to determine the growth stages based on drone remote estimation of the cabbage height and evaluate the impact of the soil texture on cabbage height. Time series variation according to the growth of Kimchi cabbage exhibits an S-shaped sigmoid curve. The logistic model of the growth curve indicates the height and growth variation of Kimchi cabbage, and the growth rate and growth acceleration formula of Kimchi cabbage can thus be derived. The curvature of the growth parameter can be used to identify variations in Kimchi cabbage height and its stages of growth. The main research results are as follows. (1) According to the growth curve, Kimchi cabbage growth can be divided into four stages: initial slow growth stage (seedling), growth acceleration stage (transplant and cupping), heading through slow growth, and final maturity. The three boundary points of the Kimchi cabbage growth curve are 0.2113 Gmax, 0.5 Gmax, and 0.7887 Gmax, where Gmax is the maximum height of Kimchi cabbage. The growth rate of cabbage reaches its peak at 0.5 Gmax. The growth acceleration of cabbage forms inflection points at 0.2113 Gmax and 0.7887 Gmax, and shows a variation characteristic. (2) The produced logistic growth model expresses the variation in the cabbage surface model value for each date of cabbage observation under each soil texture condition, with a high degree of accuracy. The accuracy evaluation showed that R2 was at least 0.89, and the normalized root-mean-square error (nRMSE) was 0.09 for clay loam, 0.06 for loam, and 0.07 for sandy loam, indicating a very strong regression relationship. It can be concluded that the logistic model is an important model for the phase division of cabbage growth and height variation based on cabbage growth parameters. The results obtained in this study provide a new method for understanding the characteristics and mechanisms of the growth phase transition of cabbage, and this study will be useful in the future to extract various types of information using drones and sensors from field vegetable crops.

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“…In particular, in South Korea, which is highly reliant on imports, it is very important to understand the current status of the cultivation area and production in order to stabilize consumer prices, make decisions on grain import policies, and efficiently inventory selfsufficiency. The crop cultivation area survey conducted in the past was carried out by selecting a sample and visiting the site in person or by conducting a cultivation intention survey by interview [6]. It is very difficult to obtain objective data because the survey results depend mainly on observations and can be subjective depending on the skill level of the investigator [7].…”

Section: Introductionmentioning

confidence: 99%

UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area

Lee

Kim²,

Park

2021

Agronomy

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South Korea’s agriculture is characterized by a mixture of various cultivated crops. In such an agricultural environment, convergence technology for ICT (information, communications, and technology) and AI (artificial intelligence) as well as agriculture is required to classify objects and predict yields. In general, the classification of paddy fields and field boundaries takes a lot of time and effort. The Farm Map was developed to clearly demarcate and classify the boundaries of paddy fields and fields in Korea. Therefore, this study tried to minimize the time and effort required to divide paddy fields and fields through the application of the Farm Map. To improve the fact that UAV image processing for a wide area requires a lot of time and effort to classify objects, we suggest a method for optimizing cultivated crop recognition. This study aimed to evaluate the applicability and effectiveness of machine learning classification techniques using a Farm Map in object-based mapping of agricultural land using unmanned aerial vehicles (UAVs). In this study, the advanced function selection method for object classification is to improve classification accuracy by using two types of classifiers, support vector machine (SVM) and random forest (RF). As a result of classification by applying a Farm Map-based SVM algorithm to wide-area UAV images, producer’s accuracy (PA) was 81.68%, user’s accuracy (UA) was 75.09%, the Kappa coefficient was 0.77, and the F-measure was 0.78. The results of classification by the Farm Map-based RF algorithm were as follows: PA of 96.58%, UA of 92.27%, a Kappa coefficient of 0.94, and the F-measure of 0.94. In the cultivation environment in which various crops were mixed, the corn cultivation area was estimated to be 96.54 ha by SVM, showing an accuracy of 90.27%. RF provided an estimate of 98.77 ha and showed an accuracy of 92.36%, which was higher than that of SVM. As a result of using the Farm Map for the object-based classification method, the agricultural land classification showed a higher efficiency in terms of time than the existing object classification method. Most importantly, it was confirmed that the efficiency of data processing can be increased by minimizing the possibility of misclassification in the obtained results. The obtained results confirmed that rapid and reliable analysis is possible when the cultivated area of crops is identified using UAV images, a Farm Map, and machine learning.

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Applicability Evaluation of Agricultural Subsidies Inspection Using Unmanned Aerial Vehicle

Cited by 8 publications

References 0 publications

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Analysis of Growth Characteristics of Kimchi Cabbage Using Drone-Based Cabbage Surface Model Image

UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area

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