• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.3
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• pp.65-74
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• 2014

The aim of this study was to evaluate the monitoring efficiency of camera trapping in wild animals and to determine ways to increase its utilization. Nineteen sensor cameras were installed in Sobaeksan National Park from October 2012 to September 2013. During the study period, a total of 1045 terrestrial mammal photos were secured and 15 species habitats were identified. Shooting frequency was higher for medium and large mammals, especially full images of carnivores accounted for approximately 83%. A comparison of track surveys revealed that camera trapping was highly efficient and helped in capturing real image of species. The supply of lure and bait stimulates the sense of smell in carnivores, which further enhances the capturing of images by camera trapping. The results of this study provide data on the ecological characteristics of mammals, which can aid in determining habitat use by these animals, and thereby facilitate prevention of crop damage by wildlife.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.253-259
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat + ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by National Geographic Information based on aerial photograph and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The classification result by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.98-103
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• 2019

A more accurate method of feature point extraction and matching for three-dimensional reconstruction using low-resolution images of crops is proposed herein. This method is important in basic computer vision. In addition to three-dimensional reconstruction from exact matching, map-making and camera location information such as simultaneous localization and mapping can be calculated. The results of this study suggest applicable methods for low-resolution images that produce accurate results. This is expected to contribute to a system that measures crop growth condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• Journal of the Korean association of regional geographers
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• v.11 no.4
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• pp.453-462
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• 2005

The study deals with surface erosion patterns due to the development of cropland toward hillslope and hilltop in the Oncheon-gun, pyeongbuk province and Nampo city of west coast area in the North Korea, using Quick Bird satellite images with 60cm resolution. In North Korea, for national economic difficult after 1980 year, newly developed croplands have been along the gentle hillslope, in which it is possible for individual man power different from the tideland which needs large scaled man-power and equipment. The new croplands are named Darakbat(terraced farm with embankment) and Bitalbat(titled farm developed on the original hill slope), neighboring with orchard and grouped settlement in lower valley. For supplying agricultural water, irrigation ditches and temporal crop storages have been constructed, connecting Darakbat, Bitalbat and orchard. These cropland developments have caused surface erosion composed of 3 types such as pit, linear and headward erosion, together with rill and gully. Owing to poor management of cropland and irrigation system, topsoil erosion and, collapse and sedimentation of ditch and pool, caused the decrease of agricultural productivity. These analysis using Quick Bird images can suggest original raw data about geographical facts on North Korea agriculture and help to recover their agricultural system and plan future national unified land.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.183-183
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• 2017

In the early part of rice growth, root volume primarily limits the amount of plant-accessible nitrogen (N). Therefore, knowledge of the root development is important for modeling N uptake of rice. The timing when the volume of rhizosphere cover the whole soil is also important to carry out timely top dressing. However, information about initial root expansion and associated N uptake is limited due to intrinsic technical difficulties in assessing below-ground processes. Some studies, however, showed a close relationship between below-ground root and above-ground leaf development, suggesting a possibility that above-ground attributes could serve as surrogates for the root processes. In this study, we investigated the relationship between below-ground and above-ground development of rice. Field experiments were conducted where we cultivated Koshihikari (a leading cultivar in Japan) for four different cropping schedules in 2012. In 2016, Gimbozu (HEG4) and three flowering time mutant lines of Gimbozu (X61 (se13), HS276 (ef7), DMG9 (se13, ef7)) were examined for a single season. Experiments were performed with three replications in a completely randomized design. We monitored ammonium-N concentration ([NH4+-N]) in soil solution by repeatedly taking samples from a porous tubing (10-cm long) vertically inserted at the most distant point from surrounding rice hills. Samples were taken in triplicate (= triplicate tubes) and every three days from transplanting in each experimental unit. For above-ground attributes, leaf area index (LAI) was measured in 2012, whereas soil coverage ratio was estimated by image processing in 2016. Results showed that [NH4+-N] increased gradually after transplanting and then rapidly decreased from a certain day. This distinct drop in [NH4+-N] informed us the timing at which the rice root system reached the point of porous tubing and thus essentially covered the whole soil volume. The LAI at the dropping point was about 0.43 regardless of the cropping schedules in 2012 experiment. In 2016, the coverage ratio at the N dropping point was within the range of 0.12 to 0.19 for four genotypes having different growth durations. In addition, the coverage ratios at seven weeks after the transplanting showed a good correspondence to LAI across the four genotypes. We therefore conclude that both LAI and coverage ratio may serve as robust indicators for root development and might be useful to estimate the timing when the root system fully cover the soil volume. Results obtained here will also contribute to develop models that can predict not only above-ground canopy development but also associated below-ground processes.