• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.7
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• pp.73-86
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• 2006

Concentrated poultry production in the State of Alabama, U.S.A. results in excessive poultry litter. Application of poultry litter to pastures and row crops serves as a cheap alternative to commercial fertilizer. However, over the years, poultry litter application to perennial forage crops in the Appalachian Plateau region of North Alabama has resulted in phosphorus (P) buildup in soils. Phosphorus index (P-index) and comprehensive nutrient management plans (CNMP) are often used as a best management practice (BMP) for proper land application of litter. Because nutrient management planning is often not done for small animal feeding operations (AFOs), and also because, in case of excess litter, litter transportation infrastructure has not been developed, over application of poultry litter to near by area is a common practice. To alleviate this problem, optimal poultry litter management and transportation infrastructure needs to be developed. This paper presents a methodology to optimize poultry litter application and transportation through efficient nutrient management planning and transportation network analysis. The goal was accomplished through implementation of three important modules, a P-Index module, a CNMP module, and a transportation network analysis module within ArcGIS, a Geographic Information System (GIS). The CNMP and P-Index modules assist with land application of poultry litter at a rate that is protective of water quality, while the transportation network analysis module helps transport excess litter to areas requiring litter in the Appalachian Plateau and Black Belt (a nutrient-deficient area) regions. Once fully developed and implemented, such a system will help alleviate water quality problems in the Appalachian Plateau region and poor soil fertility problems in the Black Belt region by optimizing land application and transportation. The utility of the methodology is illustrated through a hypothetical case study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.47
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• pp.107-123
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• 2002

This paper is intented to present a information of various aspects of quality related characteristics and standards for grades in maize. Maize is world's one of the three most popular cereal crops and a primary energy supplement and can contribute up to 30, 60, and 98% of the dairy diet's protein, net energy, and starch, respectively. Maize is also processed into industrial goods by wet or dry milling. Sweet corn is a leader among vegetable crops and its production for fresh or processing markets is a major industry in many countries. Over the years, the combined efforts of breeders and geneticists, biochemists, food scientists, and others have helped bring us to the point where we understand issues related to sweet corn quality. Traditional criteria for selecting corn hybrids have been based primarily on agronomic factors, including grain production, disease resistance, drought tolerance, and storage characteristics. Little emphasis has been placed on the quality and nutritional values of corn. Although there is widespread interest for value-enhanced corns have increased tremendously in the last five years, there is limited information available on the production and comparing the quality attributes of specialty grains with those of normal yellow dent corn. Most countries have developed national maize standards, aiming to provide a framework for trade, both internal and external. Where trading involves direct choice and price negotiation in front of the commodity, grading standards are rarely employed; quality is assessed visually and is influenced by end-use, and the price is determined more by local rather than national factors. The use of an agreed standard will provide an unambiguous description of the quality of the consignment and assist in the formation of a legally-binding contract. Standards can also be seen to protect consumers rights through setting limits to the amount of unsuitable or noxious material.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1423-1431
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• 2018

IoT(Internet of Things) is applied to technologies such as agriculture and dairy farming, making it possible to cultivate crops easily and easily in cities.In particular, IoT technology that intelligently judge and control the growth environment of cultivated crops in the agricultural field is being developed. In this paper, we propose a method of predicting the growth environment of plants by learning the moisture supply cycle of plants using the intelligent object internet. The proposed system finds the moisture level of the soil moisture by mapping learning and finds the rules that require moisture supply based on the measured moisture level. Based on these rules, we predicted the moisture supply cycle and output it using media, so that it is convenient for users to use. In addition, in order to reduce the error of the value measured by the sensor, the information of each plant is exchanged with each other, so that the accuracy of the prediction is improved while compensating the value when there is an error. In order to evaluate the performance of the growth environment prediction system, the experiment was conducted in summer and winter and it was verified that the accuracy was high.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.339-345
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• 2017

Recently, agriculture and the environment has changed dramatically due to global warming and abnormal weather. In particular, it is necessary to develop new agricultural techniques according to transforming the growing environment of agricultural crops. Therefore, "Smart Farm" building technology for controlling agricultural environment and improving efficiency for ICT technology development has recently been introduced. However, in reality, systematic and objective evaluation items are absent at various levels and management levels that affect the management environment of the smart farm. In this research, it derived the importance index among the factors associated with Smart Farm technology by AHP method. As a result, in order to evaluate comprehensive operation and management of the smart farm, the two evaluation fields(sensor device and control/information management system) were selected as the top evaluation items. These results mean that system that can detect the growth environment information of agricultural crops and control the growing environment is more important than anything, when smart farm is applied. It is judged that the results of this research can be used as basic data for making evaluation indicators associated with the introduction of smart palm technology in the future.

• Journal of Food Hygiene and Safety
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• v.32 no.6
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• pp.500-506
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• 2017

Kimchi cabbage is one of the four major vegetable crops in Korea. The total annual production of kimchi cabbage, the main material of kimchi, was 20,559 tons in 2015. Kimchi cabbage is one of the majer crops produced by farmers which accounts for about 80% of the total leaf vegetable production in Korea. As the consumption of environmental-friendly agricultural products increases, food safety is one of the major public health concerns. We analyzed the biological hazards of kimchi cabbage produced by two types of cultivation methos such as organic farming and conventional farming using various culture media and microscopy. A total of 432 samples were analysed for presence of sanitary indicator microorganisms (aerobic plate count, coliform count, yeast & mold) and food-borne pathogens (Staphylococcus aureus, environmental Listeria, Bacillus cereus). The population of sanitary indicating microorganisms and food borne pathogens was under 5 Log CFU/g in all tested samples. The results of total microorganism numbers of leaf surface showed a positive correlation to those of soil samples. Additionally, we examined chemical factors such as pesticide residues and heavy metals in soil samples. All tested samples did not shown contamination levels higher than the standard limit.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.23-29
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• 2020

The National Institute of Agricultural Sciences of the Rural Developement Administration (NAS, RDA) is conducting various studies on various crops, such as monitoring the cultivation environment and analyzing harvested seeds for high-throughput phenotyping. In this paper, we propose a deep learning-based rice seed segmentation method to analyze the seeds of various crops owned by the NAS. Using Mask-RCNN deep learning model, we perform the rice seed segmentation from manually taken images under specific environment (constant lighting, white background) for analyzing the seed characteristics. For this purpose, we perform the parameter tuning process of the Mask-RCNN model. By the proposed method, the results of the test on seed object detection showed that the accuracy was 82% for rice stem image and 97% for rice grain image, respectively. As a future study, we are planning to researches of more reliable seeds extraction from cluttered seed images by a deep learning-based approach and selection of high-throughput phenotype through precise data analysis such as length, width, and thickness from the detected seed objects.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.1-11
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• 2021

Soil moisture plays a critical role in hydrological processes, land-atmosphere interactions and climate variability. It can limit vegetation growth as well as infiltration of rainfall and therefore very important for agriculture sector and food protection. Recently, due to the increased damage from drought caused by climate change, there is a frequent occurrence of shortage of agricultural water, making it difficult to supply and manage stable agricultural water. Efficient water management is necessary to reduce drought damage, and soil moisture management is important in case of upland crops. In this study, soil moisture was calculated based on the water balance model, and the suitability of soil moisture data was verified through the application. The regional soil moisture was calculated based on the meteorological data collected by the meteorological station, and applied the Runs theory. We analyzed the spatiotemporal variability of soil moisture and drought impacts, and analyzed the correlation between actual drought impacts and drought damage through correlation analysis of Standardized Precipitation Index (SPI). The soil moisture steadily decreased and increased until the rainy season, while the drought size steadily increased and decreased until the rainy season. The regional magnitude of the drought was large in Gyeonggi-do and Gyeongsang-do, and in winter, severe drought occurred in areas of Gangwon-do. As a result of comparative analysis with actual drought events, it was confirmed that there is a high correlation with SPI by each time scale drought events with a correlation coefficient.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.619-629
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• 2023

Fertilizers are crucial for increasing crop yield, but using too much of them without taking into account the nutrients that the crops need can increase costs for farm management and have a negative impact on the environment. Through smart agriculture, fertilizers can be applied as needed at the right time to reflect the growth characteristics of crops, reducing the burden of fertilizer losses and providing economical nutrient management. In this study, we use the total dry weight of field-cultivated red pepper and green onion grown in various growing environments to fit a nonlinear model-based crop growth model using different growth curves (logistic, Gompertz, Richards, and double logistic curve), and we propose a fertilizer distributed algorithm based on crop growth rate.

• Agribusiness and Information Management
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• v.9 no.1
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• pp.7-17
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• 2017

Agricultural water management has gained enormous attention in the developing world to alleviate poverty, reduce hunger and conserve ecosystems in small-scale production systems of resource-poor farmers. The story of food security in the $21^{st}$ century in India is likely t o be closely linked to the story of water security. Today, the water resource is under severe threat. The past experiences in India in general and in Andhra Pradesh in particular, indicated inappropriate management of irrigation has led to severe problems like excessive water depletion, reduction in water quality, water logging, salinization, marked reduction in the annual discharge of some of the rivers, lowering of ground water tables due to pumping at unsustainable rates, intrusion of salt water in some coastal areas etc. Considering the importance of irrigation water resource efficiency, Krishna Western Delta (KWD) of Andhra Pradesh was purposively selected for this in depth study, as the farming community in this area are severely affected due to severe soil salinity and water logging problems and hence, adoption of different water saving crop production technologies deserve special mention. It is quite disappointing that, canals, tube wells and filter points and other wells could not contribute much to the irrigated area in KWD. Due to less contribution from these sources, the net area irrigated also showed declining growth at a rate of -6.15 per cent. Regarding paddy production, both SRI and semi-dry cultivation technologies involves less irrigation cost (Rs. 2475.21/ha and Rs. 3248.15/ha respectively) when compared to transplanted technology (Rs. 4321.58/ha). The share of irrigation cost in Total Operational Cost (TOC) was highest for transplanted technology of paddy (11.06%) followed by semi-dry technology (10.85%) and SRI technology (6.21%). The increased yield and declined cost of cultivation of paddy in SRI and semi-dry production technologies respectively were mainly responsible for the low cost of production of paddy in SRI (Rs. 495.22/qtl) and semi-dry (Rs. 532.81/qtl) technologies over transplanted technology (Rs. 574.93/qtl). This clearly indicates that, by less water usage, paddy returns can be boosted by adopting SRI and semi-dry production technologies. Both the system-level and field-level interventions should be addressed to solve the issues/problems of water management. The enabling environment, institutional roles and functions and management instruments are posing favourable picture for executing the water management interventions in the State of Andhra Pradesh in general and in KWD in particular. This facilitates the farming community to harvest good crop per unit of water resource used in the production programme. To achieve better results, the Farmers' Organizations, Water Users Associations, Department of Irrigation etc., will have to aim at improving productivity per unit of water drop used and this must be supported through system-wide enhancement of water delivery systems and decision support tools to assist farmers in optimizing the allocation of limited water among crops, selection of crops based on farming situations, and adoption of appropriate alternative crops in drought years.