• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.739-744
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• 2002

Recently we have discovered that sediments should be separated from lithosphere, and soil should be separated from biosphere, both sediment and soil will be mixed sediments-soil-sphere (Seso-sphere), which is using particulate mechanics to be solved. Erosion and sediment both are moving by particulate matter with water or wind. But ancient sediments will be erosion same to soil. Nowadays, real soil has already reduced much more. Many places have only remained sediments that have ploughed artificial farming layer. Thus it means sediments-soil-sphere. This paper discusses sediments-soil-sphere erosion modeling. In fact sediments-soil-sphere erosion is including water erosion, wind erosion, melt-water erosion, gravitational water erosion, and mixed erosion. We have established geographical remote sensing information modeling (RSIM) for different erosion that was using remote sensing digital images with geographical ground truth water stations and meteorological observatories data by remote sensing digital images processing and geographical information system (GIS). All of those RSIM will be a geographical multidimensional gray non-linear equation using mathematics equation (non-dimension analysis) and mathematics statistics. The mixed erosion equation is more complex that is a geographical polynomial gray non-linear equation that must use time-space fuzzy condition equations to be solved. RSIM is digital image modeling that has separated physical factors and geographical parameters. There are a lot of geographical analogous criterions that are non-dimensional factor groups. The geographical RSIM could be automatic to change them analogous criterions to be fixed difference scale maps. For example, if smaller scale maps (1:1000 000) that then will be one or two analogous criterions and if larger scale map (1:10 000) that then will be four or five analogous criterions. And the geographical parameters that are including coefficient and indexes will change too with images. The geographical RSIM has higher precision more than mathematics modeling even mathematical equation or mathematical statistics modeling.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.273-284
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• 2018

Purpose: Conventional headland turning typically requires repeated forward and backward movements to move the farming equipment to the next row. This research focuses on developing an upland agricultural robot with an optimized headland turning mechanism that enables a $180^{\circ}$ turning positioning to the next row in one steering motion designed for a two-wheel steering, four-wheel drive agricultural robot named the HADA-bot. The proposed steering mechanism allows for faster turnings at each headland compared to those of the conventional steering system. Methods: The HADA-bot was designed with 1.7-m wide wheel tracks to travel along the furrows of a garlic bed, and a look-ahead path following algorithm was applied using a real-time kinematic global positioning system signal. Pivot turning tests focused primarily on accuracy regarding the turning radius for the next path matching, saving headland turning time, area, and effort. Results: Several test cases were performed by evaluating right and left turns on two different surfaces: concrete and soil, at three speeds: 1, 2, and 3 km/h. From the left and right side pivot turning results, the percentage of lateral deviation is within the acceptable range of 10% even on the soil surface. This U-turn scheme reduces 67% and 54% of the headland turning time, and 36% and 32% of the required headland area compared to a 50 hp tractor (ISEKI, TA5240, Ehime, Japan) and a riding-type cultivator (CFM-1200, Asia Technology, Deagu, Rep. Korea), respectively. Conclusion: The pivot turning trajectory on both soil and concrete surfaces achieved similar results within the typical operating speed range. Overall, these results prove that the pivot turning mechanism is suitable for improving conventional headland turning by reducing both turning radius and turning time.

• Animal Bioscience
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• v.34 no.7
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• pp.1235-1242
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• 2021

Objective: The purpose of this study was to identify through infrared thermal imaging technology the facial surface temperature (FST) of laying hens in response to the variations in their thermal environment, and to identify the regional differences in FST to determine the most stable and reliable facial regions for monitoring of thermoregulatory status in chickens. Methods: Thirty Hy-Line Brown hens (25-week-old) were sequentially exposed to three different thermal conditions; optimal (OT, 22℃±2℃), low (LT, 10℃±4℃), and high temperature (HT, 30℃±2℃). The mean values of FST in five facial regions including around the eyes, earlobes, wattles, beak and nose, and comb were recorded through infrared thermography. The maximum FST (MFST) was also identified among the five face-selective regions, and its relationship with temperature-humidity index (THI) was established to identify the range of MFST in response to the variations in their thermal environment. Results: Hens exposed to OT condition at 15:00 displayed a higher temperature at wattles and around the eyes compared to other regions (p<0.001). However, under LT condition at 05:00 to 08:00, around the eyes surface temperature showed the highest value (p<0.01). In HT, wattles temperature tended to show the highest temperature over almost time intervals. Main distribution regions of MFST were wattles (63.3%) and around the eyes (16.7%) in OT, around the eyes (50%) in LT, and wattles (62.2%) and comb (18.3%) in HT. The regression equation between MFST and THI was estimated as MFST = 35.37+0.2383×THI (R² = 0.44; p<0.001). Conclusion: The FST and the frequency of MFST in each facial region of laying hens responded sensitively to the variations in the thermal environment. The findings of this experiment provide useful information about the effect of the thermal conditions on the specific facial regions, thus offering an opportunity to stress and welfare assessment in poultry research and industry.

• Journal of Practical Agriculture & Fisheries Research
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• v.10 no.1
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• pp.29-41
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• 2008

This research was conducted to develop a technology transfer and farmer's extension of newly released technology of Bokto seeding method for crop and vegetable production based on the theory of Asian and Pacific Center for Transfer of Technology(APCTT). This technology has recently transferred to not only Korea but also other countries like North Korea, China, Japan, Taiwan, Russia and Africa(Cameroon, Sudan and South Africa) since 2005. It has known as a highly reduction of production cost in terms of labors, chemical fertilizer and pesticides as well as environmental friendly due to a deep and side banded placement of chemical fertilizer at basal application. In addition this technology was proven to a precision farming on sowing depth and mechanism of chemical application method and also highly resistant against disasters like typhoon, flooding, low temperature, drought and lodging due to silicate application. It has improved a constraints such as a poor seedling establishment, weed occurrence, lodging, low yield and poor grain and eating quality in the previous direct seeding methods but still have a problem in occurrence of weedy rice and ununiformed operation of wet or flooded soil condition. Also this technology has a limit in marketing and A/S system. Based on a theory of APCTT evaluation and analysis this technology may be more concentrated on establishment of a special cooperation team among researcher and scientists, extension workers, industry sections and governmental sectors in order to rapidly transfer this technology to farmer's field. Also there will be needed to operate a web site for this newly released technology to inform and exchange an idea, experiences and newly improved information. A feed back system might be operated in this technology as well to improve a technology under way on users' operation. Also user's manual will be internationally released and provided for farmer's instruction and training at field site.

• 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.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.369-378
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• 2006

For precision farming the influences of site-specific N application on rice grain yield and quality were investigated in 0.5 ha paddy field from 2001 to 2003. In pre-cultured soil, EC, O.M., total nitrogen, phosphate and potassium content showed high spatial variation, ranging from 11.63 to 52.03% of coefficient of variation, while that of pH was relatively low. In rice growth characteristics, tiller number at panicle formation stage was more than 10% in coefficient of variation, but plant height, SPAD figure at panicle formation stage and milled rice yield, protein content in brown rice showed less below 10%. Spatial dependence was over 0.60 in pH, total nitrogen, phosphate and potassium in pre-cultured soil and was over 0.50 in plant height, SPAD figure and protein content, while it was below 0.22 in tiller number at panicle formation. The range of spatial dependence was longer than 20m in all factors except for protein content in brown rice. Basal dressing nitrogen rate was positively correlated with PH, $SiO_{2}$, plant height and SPAD figure. Nitrogen fertilization rate at panicle formation stage was positively correlated with EC and O.M.. Protein content in brown rice was positively correlated with $SiO_{2}$ in pre-cultured soil. Milled rice yield was positively correlated with plant height, tiller number and SPAD figure at panicle formation stage.

• Journal of Biosystems Engineering
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• v.30 no.1 s.108
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• pp.45-55
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• 2005

Knowledge of the relationship between crop yield and yield-limiting factors is essential for precision farming. However, developing this knowledge is not easy because these yield-limiting factors are interrelated and affect crop yield in different ways. In this study, data for grain yield and yield-limiting factors, including crop chlorophyll content, soil chemical properties, and topography were collected for a small (0.3 ha) rice paddy field in Korea and a large (36 ha) upland corn field in the USA, and relationships were investigated with path analysis. Using this approach, the effects of limiting factors on crop yield could be separated into direct effects and indirect effects acting through other factors. Path analysis provided more insight into these complex relationships than did simple correlation or multiple linear regression analysis. Results of correlation analysis for the rice paddy field showed that EC, Ca, and $SiO_2$ had significant (P<0.1) correlations with rice yield, while pH, Ca, Mg, Na, $SiO_2,\;and\;P_2O_5$ had significant correlations with the SPAD chlorophyll reading. Path analysis provided additional information about the importance and contribution paths of soil variables to rice yield and growth. Ca had the highest direct effect (0.52) and indirect effect via Mg (-0.37) on rice yield. The indirect effect of Mg through Ca (0.51) was higher than the direct effect (-0.38). Path analysis also enabled more appropriate selection of important factors limiting crop yield by considering cause-and-effect relationships among predictor and response variables. For example, although pH showed a positive correlation (r=0.35) with SPAD readings, the correlation was mainly due to the indirect positive effects acting through Mg and $SiO_2$, while pH not only showed negative direct effects, but also negatively impacted indirect effects of other variables on SPAD readings. For the large upland Missouri corn field, two topographic factors, elevation and slope, had significant (P<0.1) direct effects on yield and highly significant (P<0.01) correlations with other limiting factors. Based on the correlation analysis alone, P and K were determined to be nutrients that would increase corn yield for this field. With the help of path analysis, however, increases in Mg could also be expected to increase corn yield in this case. In general, path analysis results were consistent with published optimum ranges of nutrients for rice and com production. We conclude that path analysis can be a useful tool to investigate interrelationships between crop yield and yield limiting factors on a site-specific basis.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1062-1062
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• 2001

The concept of “precision agriculture” or “site-specific farming” is usually confined to the fields of soil science, crop science and agronomy. However, because plants grow in soil, animals eat plants, and humans eat animal products, it could be argued (perhaps with some poetic licence) that the fields of feed quality, animal nutrition and animal production should also be considered in this context. NIR spectroscopy has proved over the last 20 years that it can provide a firm foundation for quality measurement across all of these fields, and with the continuing developments in instrumentation, computer capacity and software, is now a major cog in the wheel of precision agriculture. There have been a few giant leaps and a lot of small steps in the impact of NIR on the animal world. These have not been confined to the amazing advances in hardware and software, although would not have occurred without them. Rapid testing of forages, grains and mixed feeds by NIR for nutritional value to livestock is now commonplace in commercial laboratories world-wide. This would never have been possible without the pioneering work done by the USDA NIR Forage Research Network in the 1980's, following the landmark paper of Norris et al. in 1976. The advent of calibration transfer between instruments, algorithms which utilize huge databases for calibration and prediction, and the ability to directly scan whole grains and fresh forages can also be considered as major steps, if not leaps. More adventurous NIR applications have emerged in animal nutrition, with emphasis on estimating the functional properties of feeds, such as in vivo digestibility, voluntary intake, protein degradability and in vitro assays to simulate starch digestion. The potential to monitor the diets of grazing animals by using faecal NIR spectra is also now being realized. NIR measurements on animal carcasses and even live animals have also been attempted, with varying degrees of success, The use of discriminant analysis in these fields is proving a useful tool. The latest giant leap is likely to be the advent of relatively low-cost, portable and ultra-fast diode array NIR instruments, which can be used “on-site” and also be fitted to forage or grain harvesters. The fodder and livestock industries are no longer satisfied with what we once thought was revolutionary: a 2-3 day laboratory turnaround for fred quality testing. This means that the instrument needs to be taken to the samples rather than vice versa. Considerable research is underway in this area, but the challenge of calibration transfer and maintenance of instrument networks of this type remains. The animal world is currently facing its biggest challenges ever; animal welfare, alleged effects of animal products on human health, environmental and economic issues are difficult enough, but the current calamities of BSE and foot and mouth disease are “the last straw” NIR will not of course solve all these problems, but is already proving useful in some of these areas and will continue to do so.