• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.420-428
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• 2016

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And $NDVI_{UAV}$ and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to $NDVI_{UAV}$ and other agro-meteorological factors were well reflected in the model.

• Journal of Biosystems Engineering
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• v.34 no.2
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• pp.95-105
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• 2009

A method was proposed which employed control of the drop location of fertilizer particles on a spinner disc to optimize the spread pattern uniformity. The system contained an optical sensor as a feedback mechanism, which measured discharge velocity and location, as well as particle diameters to predict a spread pattern of a single disc. Simulations showed that the feed gate adaptation algorithm produced high quality patterns for any given application rate in the dual disc spreader. The performance of the feed gate control method was assessed using data collected from a Sulky spinner disc spreader. The results showed that it was always possible to find a spread pattern with an acceptable CV lower than 15%, even though the spread pattern was obtained from a rudimentary flat disc with straight radial vanes. A mathematical optimization method was used to find the initial parameter settings for a specially designed experimental spreading arrangement, which included the feed gate control system, for a given flow rate and swath width. Several experiments were carried out to investigate the relationship between the gate opening and flow rate, disc speed and particle velocity, as well as disc speed and predicted landing location of fertilizer particles. All relationships found were highly linear ($r^2$ > 0.96), which showed that the time-of-flight sensor was well suited as a feedback sensor in the rate and uniformity controlled spreading system.

• Journal of Applied Biological Chemistry
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• v.59 no.2
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• pp.165-169
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• 2016

The quantitative analytical method for the bioactive substance, 3-cyano-4-methoxy-N-methyl-2-pyridone (ricinine) and an index compound, ricinoleic acid in castor plant (Ricinus communis) extract or oil was developed. For the determination of a pyridone alkaloid compound, ricinine, successive cartridge cleanup method combined with ultra-performance liquid chromatography was set up with $ENVI-Carb^{TM}$ (0.5 g) and $C_{18}$ SPE cartridges. Accuracy and precision were evaluated through fortification studies of one biopesticide (PE) at 10 and $100mg\;kg^{-1}$. Mean recoveries of ricinine were 98.7 and 96.0 % associated with less than 10 % RSD, respectively. For the determination of ricinoleic acid in castor extract and oil, saponification and methylation were optimized using gas chromatography-time of flight mass spectrometry. Recovery was more than 84.8 % associated with 6.2 % RSD after derivatization procedure. Both methodologies developed were applied to analyze real samples including three castor oil products and six commercially available biopesticides containing R. communis, collected at Korean market. The contents of ricinine and ricinoleic acid in most commercial biopesticides were less than the oil or extract contents indicated by label.

• Geophysics and Geophysical Exploration
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• v.21 no.4
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• pp.244-254
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• 2018

The first geophysical technique applied to the agricultural sector in Korea was electrical resistivity sounding and conducted in purpose of groundwater exploitation in the 1970s. According to the diversity of agricultural activities since the 1990s, various geophysical methods including electrical resistivity, electromagnetic induction, and self-potential method were applied to several agricultural fields such as soil characterization with saline concentration in vast reclaimed area, delineation of seawater intrusion regions in costal aquifer, safety inspection of embankment dikes with leakage problem, detection of ground subsidence from overpumping and tracing of groundwater aquifer contamination by leachate from livestock mortality burial or waste burial site. This paper introduces representative geophysical techniques that have been utilized in various agricultural fields and suggests several ways to develop the geophysical methods required for the precision agriculture field in the near future based on the past achievements.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1311-1317
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• 2018

The ultimate goal of satellite development is to use information obtained from satellites. Therefore, national-levelsatellite development program should include not only hardware development, but also infrastructure establishment and application technology development for information utilization. Until now, Korea has developed various satellites and has been very useful in weather and maritime surveillance as well as various disasters. In particular, KOMPSAT (Korea Multi-purpose Satellite) images have been used extensively in agriculture, forestry and marine fields based on high spatial resolution, and has been widely used in research related to precision mapping and change detection. This special issue aims to introduce a variety of recent studies conducted using KOMPSAT optical and SAR (Synthetic Aperture Radar) images and to disseminate related satellite image application technologies to the public sector.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.175-181
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• 2022

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

• Journal of Forest and Environmental Science
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• v.39 no.3
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• pp.180-194
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• 2023

This study characterize, classify and evaluates the function of topography on spatial variability of some selected soil properties to assist in designing land management that support uniform agricultural production. The study site, an agricultural land, was part of the derived savanna zone in southwest Nigeria. Four soil profile pits each were established along two delineated toposequence and described following the FAO/UNESCO guidelines. Samples were collected from the identified genetic horizons. Properties of four soil series developed on different positions of the two delineated Toposequence viz upper, middle, lower slopes and valley bottom positions respectively were studied. The soil samples were analysed for selected physical and chemical properties and data generated were subjected to descriptive and inferential statistics. The results showed that soil colour, depth and texture varied in response to changes in slope position and drainage condition. The sand content ranged from 61 to 90% while the bulk density ranged between 1.06 g cm^-3 to 1.68 g cm^-3. The soils were neutral to very strongly acid with low total exchangeable bases. Available phosphorus value were low while the extractable micronutrient concentration varied from low to medium. Soils of Asejire and Iwo series mapped in the study area were classified as Typic isohyperthermic paleustult, Apomu series as Plinthic isohyperthermic paleustult and Jago series as Aquic psamment (USDA Soil Taxonomy). These soils were correlated as Lixisol, Plinthic Lixisol and Fluvisol (World Reference Based), respectively. Major agronomic constraints of the soils associations mapped in the study area were nutrient availability, nutrient retention, slope, drainage, texture, high bulk density and shallow depth. The study concluded that the soils were not homogenous, shows moderate spatial variation across the slope, had varying potentials for sustainable agricultural practices, and thus, the agronomic constraints should be carefully addressed and managed for precision agriculture.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1055-1066
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• 2023

The cultured meat industry is continuously evolving due to the collective efforts of cultured meat companies and academics worldwide. Though still technologically limited, recent reports of regulatory approvals for cultured meat companies have initiated the standards-based approach towards cultured meat production. Incidents of deception in the meat industry call for fool-proof authentication methods to ensure consumer safety, product quality, and traceability. The cultured meat industry is not exempt from the threats of food fraud. Meat authentication techniques based on DNA, protein, and metabolite fingerprints of animal meat species needs to be evaluated for their applicability to cultured meat. Technique-based categorization of cultured meat products could ease the identification of appropriate authentication methods. The combination of methods with high sensitivity and specificity is key to increasing the accuracy and precision of meat authentication. The identification of markers (both physical and biochemical) to differentiate conventional meat from cultured meat needs to be established to ensure overall product traceability. The current review briefly discusses some areas in the cultured meat industry that are vulnerable to food fraud. Specifically, it targets the current meat and meat product authentication tests to emphasize the need for ensuring the traceability of cultured meat.

• Animal Bioscience
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• v.37 no.4_spc
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• pp.786-793
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• 2024

Latin America is a culturally, geographically, politically, and economically diverse region. Agriculture in Latin America is marked by a remarkable diversity of production systems, reflecting various agroecological zones, farm sizes, and technological levels. In the last decade, the swine industry increased by 30.6%, emerging as a great contributor to food security and economic development in Latin America. Brazil and Mexico dominate the pig production landscape, together accounting for 70% of sow inventory in the region. The swine industry in Latin America is predominantly comprised of small and medium-sized farms, however, in the past 30 years, the number of pig producers in Brazil dropped by 78%, whereas pork production increased by 326%. Similar to the global pork industry, the growing demand for pork, driven by population growth and changing dietary habits, presents an opportunity for the industry with an expected growth of 16% over the next decade. The export prospects are promising, however subject to potential disruptions from global market conditions and shifts in trade policies. Among the challenges faced by the swine industry, disease outbreaks, particularly African Swine Fever (ASF), present significant threats, necessitating enhanced biosecurity and surveillance systems. In 2023, ASF was reported to the Dominican Republic and Haiti, Porcine Reproductive and Respiratory Syndrome (PRRS) in Mexico, Costa Rica, the Dominican Republic, Colombia, and Venezuela, and Porcine Epidemic Diarrhea (PED) in Mexico, Peru, the Dominican Republic, Colombia, and Ecuador. Additionally, feed costs, supply chain disruptions, and energy expenses have affected mainly the smaller and less efficient producers. The swine industry is also transitioning towards more sustainable and environmentally friendly practices, including efficient feed usage, and precision farming. Ensuring long-term success in the swine industry in Latin America requires a holistic approach that prioritizes sustainability, animal welfare, and consumer preferences, ultimately positioning the industry to thrive in the evolving global market.

• Journal of Drive and Control
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• v.21 no.2
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• pp.36-43
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• 2024

The yield is basic and necessary information in precision agriculture that reduces input resources and enhances productivity. Yield information is important because it can be used to set up farming plans and evaluate farming results. Yield monitoring systems are commercialized in the United States and Japan but not in Korea. Therefore, such a system must be developed. This study was conducted to develop a yield monitoring system that improved performance by correcting a previously developed flow sensor using a grain tank-weighing system. An impact-plated type flow sensor was installed in a grain tank where grains are placed, and grain tank-weighing sensors were installed under the grain tank to estimate the weight of the grain inside the tank. The grain flow rate and grain weight prediction models showed high correlations, with coefficient of determinations (R2) of 0.9979 and 0.9991, respectively. A main controller of the yield monitoring system that calculated the real-time yield using a sensor output value was also developed and installed in a combine harvester. Field tests of the combine harvester yield monitoring system were conducted in a rice paddy field. The developed yield monitoring system showed high accuracy with an error of 0.13%. Therefore, the newly developed yield monitoring system can be used to predict grain weight with high accuracy.