Photovoltaic (PV) power generation facilities have been built on various scales due to rapid growth in response to demand for renewable energy. Facilities built on diverse terrain and on such a scale are required to employ fast and accurate monitoring technology for stable electrical production and maintenance. The purpose of this study was to develop a technology to analyze the normal operation and failure of solar modules by acquiring images by attaching optical and thermal infrared sensors to unmanned aerial vehicles (UAVs) and producing orthographic images of temperature information. The results obtained in this study are as follows: (1) a method of using optical and thermal infrared sensors with different resolutions at the same time is able to produce accurate spatial information, (2) it is possible to produce orthographic images of thermal infrared images, (3) the analysis of the temperature fluctuation characteristics of the solar panel and cell showed that the abnormal module and cell displayed a larger temperature change than the normal module and cell, and (4) the abnormal heat generation of the panel and cell can be accurately discerned by the abnormal state panel and cell through the spatial distribution of the temperature. It is concluded that the inspection method of the solar module using the obtained UAV-based thermal infrared sensor can be useful for safety inspection and monitoring of the rapidly growing solar power generation facility.Since the mid-1970s, the Republic of Korea has started to apply solar power to homes. In the early days, solar panels were mostly used for hot water. In 2000, solar PV applications diversified, extending to household, industrial, agricultural, lake, and reservoir applications. Republic of Korea has been mainly used to utilize mountainous areas, which occupy 63% of the entire country. Many solar power plants are generally operated by using large flat terrain, but many facilities have been applied to sloped terrain conditions. In recent years, the number of PV power generation facilities installed on the water, i.e., reservoirs and lakes, has increased. Facilities located on such terrain and water incur heavy cost and require a lot of manpower to check periodic monitoring and problematic solar panels, and there is a high risk of safety problems. In order to operate the solar power plant efficiently, it is necessary to have not only a solar panel but also equipment for monitoring and maintaining the solar power plant [4].PV power plants are affected by various factors that can damage or reduce their efficiency. Selecting an optimal location for a power plant, one that maximizes power generation efficiency, is important in reducing power-generation and maintenance costs [5]. Once installed, the first time damage occurs to any part, the cell will overload and cause abnormal heat generation. After that, the entire module is short-circuited, and the overall output is lowered. Defects and losses that can occur in solar panels are caused by various factors. Decrease of po...

Kimchi cabbage grows in South Korea and is an essential ingredient for making kimchi with the kimjang method. The technique of accurately managing and monitoring crops such as kimchi cabbage plays an important role in stabilizing consumer prices. Unmanned aerial vehicles (UAVs) are expected to be used more widely in global and local agriculture. The agricultural sites at which kimchi cabbages are cultivated are affected by various climatic, terrain, and soil conditions, requiring technologies that can accurately and quickly acquire such information. UAVs and sensors are able to provide some of these data. In this study, we set up a cultivation environment for kimchi cabbage and investigated the correlation between a UAV-attached multispectral sensor and a field-portable spectroradiometer. Reflectance measurement using a spectroradiometer was performed on 99 kimchi cabbages in a Mt. Maebong testbed. We aimed to find a method for obtaining accurate vegetation information by combining the high spatial and temporal resolution information of the UAV observation with the spectral resolution of the spectroradiometer. Spectral analysis was used to identify the difference between healthy and poorly growing cabbage and to find the wavelength that most affected the growth. The hyperspectrum of the spectroradiometer reflected the accurate vegetation characteristics and contributed greatly to the identification of vegetation indices. A method for correcting the errors that occurred in the ground and UAV monitoring and the difference arising from the application of the broadband wavelength of the UAV and the single wavelength of the spectroradiometer through correlation analysis is presented. The calibration equation method was applied to UAV spatial information and was used to create a precise normalized distribution vegetation index (p-NDVI) map. The p-NDVI map was organized into four categories for the selection of cabbages with healthy (good) growth. Our results show that (1) the merged spectral analysis method was found to be more accurate and distinct than conventional methods, and (2) methods for estimating cabbage growth status showed a higher significant correlation than the UAV-based NDVI. At the maturity stage, high accuracy (R2 = 0.7816, RMSE = 0.06) was achieved for NDVI. Although this map is the result of the limited vegetation monitoring of UAV images taken during the maturity stage, it could be of great help for managing the quality and production of cabbage. However, the efficient management of highland kimchi cabbage requires continuous research under various conditions to enable periodic and systematic monitoring using UAVs and sensors.

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.

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.