• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.241-262
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• 2021

This paper reviewed the necessity of a information and communication technology (ICT)-based smart livestock system as a development strategy for the animal life industry in the future. It also predicted the trends of livestock and animal food until 2050, 30 years later. Worldwide, livestock raising and consumption of animal food are rapidly changing in response to population growth, aging, reduction of agriculture population, urbanization, and income growth. Climate change can change the environment and livestock's productivity and reproductive efficiencies. Livestock production can lead to increased greenhouse gas emissions, land degradation, water pollution, animal welfare, and human health problems. To solve these issues, there is a need for a preemptive future response strategy to respond to climate change, improve productivity, animal welfare, and nutritional quality of animal foods, and prevent animal diseases using ICT-based smart livestock system fused with the 4th industrial revolution in various aspects of the animal life industry. The animal life industry of the future needs to integrate automation to improve sustainability and production efficiency. In the digital age, intelligent precision animal feeding with IoT (internet of things) and big data, ICT-based smart livestock system can collect, process, and analyze data from various sources in the animal life industry. It is composed of a digital system that can precisely remote control environmental parameters inside and outside the animal husbandry. The ICT-based smart livestock system can also be used for monitoring animal behavior and welfare, and feeding management of livestock using sensing technology for remote control through the Internet and mobile phones. It can be helpful in the collection, storage, retrieval, and dissemination of a wide range of information that farmers need. It can provide new information services to farmers.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.210-216
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• 2001

Pinus densiflora S. et Z. has widely been distributed, and is one of the important main foret resources in Korea. Diameter and height growth patterns were estimated using non-linear algebraic difference equation, which requires two-measurement times $T_1$ and $T_2$. To maximize data use, all possible measurement interval data were derived using Lag and Put statements in the SAS. In results, of the algebraic difference equations applied, the Schumacher and the Gompertz polymorphic equations for diameter and height, respectively showed the higher precision of the fitting. In order to allow more precise estimation of growth than those of the basic Schumacher and the Gompertz, further refinement that combine biological realism as input into the equation would be necessary.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1155-1155
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• 2001

The quality of agricultural products is very important factor for consumers. In Japan, quality is sometimes more important than cost. Usually, the quality of fresh food products is determined in terms of shape, color, size, etc. However, these indices are not always associated with taste, leaving consumers to complain. Recently, two types of the fruit quality meter (a tabletop type - K-FS200 and a portable type - K-BA100, Kubota Corp.) using NIR technology were introduced in Japan. A tabletop instrument is for post harvest use and a portable one is for precision agriculture use. The both meters use the NIR region from 600nm to 1000nm in the interactance mode to determine quality factors related to taste. The instruments can measure sugar content and acidity of such fruit as apples, tomatoes, tangerines and other fruits. The measurement is timely, nondestructive and precise. For example, the coefficient of variation (CV) is less than 6% for sugar in most fruits. The K-FS200 has been evaluated in supermarkets, grading facilities, and wholesalers in Japan. The introduction of the K-FS200) has drawn attention to taste quality and its use is becoming more popular. In addition, researchers or farmers are becoming interested in measuring product ingredient not only after harvest but also during growing in the field so that they can make intelligent judgements concerning soil amendments, such as fertilizers and water, employs the fiber probe for flexible measurement and is battery powered for field use. Design of the fruit quality meters will be discussed. Applications to fruit quality will be presented.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion 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 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• Korean Journal of Agricultural Science
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• v.42 no.1
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• pp.63-71
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• 2015

Path planning is an essential part for traveling and mowing of autonomous lawn mower tractors. Objectives of the paper were to analyze operation patterns by a skilled farmer, to extract and optimize waypoints, and to demonstrate generation of formatted planned path for autonomous lawn mower tractors. A 27-HP mower tractor was operated by a skilled farmer on grass fields. To measure tractor travel and operation characteristics, an RTK-GPS antenna with a 6-cm RMS error, an inertia motion sensing unit, a gyro compass, a wheel angle sensor, and a mower on/off sensor were mounted on the mower tractor, and all the data were collected at a 10-Hz rate. All the sensor data were transferred through a software program to show the status immediately on the notebook. Planned path was generated using the program parameter settings, mileage and time calculations, and the travel path was plotted using developed software. Based on the human operation patterns, path planning algorithm was suggested for autonomous mower tractor. Finally path generation was demonstrated in a formatted file and graphic display. After optimizing the path planning, a decrease in distance about 13% and saving of the working time about 30% was achieved. Field test data showed some overlap, especially in the turning areas. Results of the study would be useful to implement an autonomous mower tractor, but further research needs to improve the performance.

• The Plant Pathology Journal
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• v.35 no.6
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• pp.553-563
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• 2019

Investigations related with factors influencing root and crown rot are rare and mainly related to farming practice and soil management. The main objective of this study was to examine broader range of factors influencing stem-base infestation of winter wheat in the field conditions. The effect of spatial distribution of infected plants on disease index (DIs) assessments was also investigated. Analysis of factors influencing DIs of crown rot of wheat demonstrated significant influence of the growing seasons (P < 0.001) and extreme fluctuations in winter temperatures (P < 0.001). In addition to that, localities together with their interaction with the growing season also significantly influenced DIs (P < 0.001). Aggregation of infected plants influenced variability of DI estimations, and it was pointed out that more extensive investigation should be conducted on broad range of DI in order to establish sampling method giving uniform sampling precision. Fusarium graminearum was shown to be predominant Fusarium species in Serbia (72.6%) using sequence-characterized amplified region analysis. Interestingly F. oxysporum was isolated in higher frequencies (27.4%) than it was reported in the literature. Given that there were no reports on the diversity of Fusarium species causing crown rot of wheat in Serbia, this study presents first report on this important subject. It also indicated that more attention should be focused on combined effects of abiotic and biotic factors influencing stem-base infestation of winter wheat. This knowledge will contribute to better understanding of factors influencing root and crown rot of wheat which would ensure sustainable disease management in the future.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.33-39
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• 2011

An optical sensing approach based on diffuse reflectance has shown potential for rapid and reliable on-site estimation of soil properties. Important sensing ranges and the resulting regression models useful for soil property estimation have been reported. In this study, a similar approach was applied to investigate the potential of reflectance sensing in estimating soil properties for Korean paddy fields. Soil cores up to a 65-cm depth were collected from 42 paddy fields representing 14 distinct soil series that account for 74% of the total Korean paddy field area. These were analyzed in the laboratory for several important physical and chemical properties. Using air-dried, sieved soil samples, reflectance data were obtained from 350 to 2500 nm on a 3 nm sampling interval with a laboratory spectrometer. Calibrations were developed using partial least squares (PLS) regression, and wavelength bands important for estimating the measured soil properties were identified. PLS regression provided good estimations of Mg ($R^2$ = 0.80), Ca ($R^2$ = 0.77), and total C ($R^2$ = 0.92); fair estimations of pH, EC, $P_2O_5$, K, Na, sand, silt, and clay ($R^2$ = 0.59 to 0.72); and poor estimation of total N. Many wavelengths selected for estimation of the soil properties were identical or similar for multiple soil properties. More important wavelengths were selected in the visible-short NIR range (350-1000 nm) and the long NIR range (1800-2500 nm) than in the intermediate NIR range (1000-1800 nm). These results will be useful for design and application of in-situ close range sensors for paddy field soil properties.

• Journal of Biosystems Engineering
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• v.34 no.4
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• pp.269-277
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• 2009

In this research, the vegetation growth and rice taste of the liquid fertilizer applied fields (LF) were compared with those of chemical fertilizer applied fields(CF) in order to confirm the possibility of methane fermentation digested sludge as liquid fertilizer using precision agriculture and remote sensing technology. In panicle initiation stage, the vegetation growth at LF was 60%~80% of it at CF and there were significant difference of nitrogen contents between CF and LF. The estimation model of nitrogen contents was established by GNDVI (R=0.607, RMSE=$1.04\;g/m^2$, n=36, p<0.01). In heading stage, vegetation growth at LF went close to it at CF as ratio of 80%~95%. The nitrogen content estimation model was also established (R=0.650, RMSE=$1.73\;g/m^2$, n=35, p<0.01) and there were significant difference of spatial variability between LF and CF. There were not significant difference of rice taste and it's elements, when three samples, which were more than twice of standard deviation, were excepted. The protein contents estimation model using GNDVI of before harvesting (R=0.700, RMSE=0.470%, n=29, p<0.01) were more suitable to predict the protein contents at harvesting comparing with it of heading stage(R=0.610, RMSE=0.521%, n=29, p<0.01).

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.255-262
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• 2016

Purpose: Fuji apples are one of the top selling exports for South Korea bringing in over $233.4 million in 2013. However, during the last few decades, about half of the Fuji apple orchards have been infected by Apple Marssonina Blotch disease (AMB), a fungal disease caused by Diplocarpon mali., which takes about 40 days to exhibit obvious visible symptoms. Infected leaves turn yellow and begin growing brown lesions. AMB promotes early defoliation and reduces the quality and quantity of apples an infected tree can produce. Currently, there is no prediction model for AMB on the market. Methods: The Precision Agriculture Laboratory (PAL) at the University of Florida (UF) has been working with the National Academy of Agricultural Science, Rural Development Administration, South Korea to investigate the use of hyperspectral data in creating an early detection method for AMB. The RDA has been researching hyperspectral techniques for disease detection at their Apple Research Station in Gunwi since 2012 and disseminates its findings to the local farmers. These farmers were surveyed to assess the state of knowledge of AMB in the area. Out of a population of about 750 growers, 111 surveys were completed (confidence interval of +/- 8.59%, confidence level of 95%, p-value of 0.05). Results: The survey revealed 32% of the farmers did not know what AMB was, but 45% of farmers have had their orchards infected by AMB. Twenty-five percent could not distinguish AMB from other symptoms. Overwhelmingly, 80% of farmers strongly believed an early detection method for AMB was necessary. Conclusions: The results of the survey will help to evaluate the outreach programs of the RDA so they can more effectively educate farmers on the identifying, treating, and mediating AMB.

• Current Research on Agriculture and Life Sciences
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• v.32 no.2
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• pp.79-84
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• 2014

The precision aerial application of agricultural unmanned helicopters has become a new paradigm for small farms with orchards, paddy, and upland fields. The needs of agricultural applications require easy and affordable control systems. Recent developments of MEMS technology based on inertial sensors and high speed DSP have enabled the fabrication of low-cost attitude system. Therefore, this study evaluates inertial MEMS sensors for estimating the attitude of an agricultural unmanned helicopter. The accuracies and errors of gyro and acceleration sensors were verified using a pendulum system. The true motion values were calculated using a theoretical estimation and absolute encoder measurement of the pendulum, and then the sensor output was compared with reference values. When comparing the sensor measurements and true values, the errors were determined to be 4.32~5.72%, 3.53~6.74%, and 3.91~4.16% for the gyro rate and x-, z- accelerations, respectively. Thus, the measurement results confirmed that the inertial sensors are effective for establishing an attitude and heading reference system (AHRES). The sensors would be constructed in gimbals for the estimating and proving attitude measurements in the following paper.