• Title/Summary/Keyword: Food and Agriculture Industry

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Quality characteristics of short bread cookies with added green whole grain rice powder (Whole Green Rice Powder를 첨가한 쇼트 브레드 쿠키의 품질 특성)

  • Paik, Seung-Hee;Lee, Eui-Seok;Hong, Soon-Taek;Ku, Ja-Hyeong;Nam, Myoung Soo
    • Korean Journal of Agricultural Science
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    • v.40 no.4
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    • pp.377-383
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    • 2013
  • Premature-green rice is typically obtained by early harvest when culms of rice still appear green in color, and the rice and its food products have been considered as wellbeing foods. This study was carried out to determine the quality characteristics of cookies made from flour added with 10, 20, and 30% whole green rice powde r(WGRP) of two kinds of Hopum and Shinsun waxy rice. The quality characteristics of cookies, including pH, spread factor, color, hardness, and sensory properties, were estimated. WGRP with different levels of 10, 20, and 30% was added into powder for preparing cookies, and their quality properties were evaluated. The pH and hardness of the cookies increased, while spread factor showed highest added with 20% Hopum and Shinsun waxy rice. The color (L) of cookies decreased 20% added with Hopum and Shinsun waxy rice. The sensory properties of cookies was highest added with 20% Hopum and 10% Shinsun waxy rice. The result of this study suggest that addition of 20% Hopum and 10% Shinsun waxy rice are available rice cookies. It was concluded that WGRP may have a potential in bakery industry as a new food material.

Study of Garlic's Carbon Footprint though LCA (전과정평가를 통한 마늘의 탄소배출량 산정연구)

  • Yoon, Sung-Yee;Kim, Young-Ran;Kim, Tae-Ho;Park, Jin-Hyun;Ahn, Sung-Woo
    • Korean Journal of Organic Agriculture
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    • v.20 no.2
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    • pp.161-172
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    • 2012
  • This study was carried out to estimate carbon footprint and to establish of LCA of garlic production system. We have case study in cultivate garlic 1 kg calculate in carbon footprint. LCA carried out to estimate carbon footprint and to establish of LCI (life cycle inventory) database of garlic production system. The data is from Research of Farmer's income in 2010 (RDA, 2011), and used Pass (5.0.0) program. The value of fertilizer, amount of pesticide input were shown the environmental effect and direct emission. Carbon footprint in agriculture guarantees the choice right the consumer to choose the lower carbon goods. Its can make to strengthen of agriculture and food industry's reduction effort of $CO_2$. Nowadays consumer requests food's safety and environment friendly process. Carbon footprint also needs consumer's relief and incentives.

Potentials of Regional Clustering: the Case of Food Industry at Gyeongsangnam-Do (식품 클러스터의 잠재성 분석: 경남지역을 중심으로)

  • Kim, Sung-Yong;Ahn, Byung-Il;Kim, Yun-Shik;Lee, Mi-Sook;Nam, Kyung-Soo;Gil, Su-Min
    • Journal of agriculture & life science
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    • v.43 no.6
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    • pp.117-127
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    • 2009
  • There are worldwidely rising interests in food cluster as it has been perceived as a strategy for improving the competitiveness of food industry. This paper examines the potential of food industry at Gyeongsangnam-do for a regional clustering in terms of five cluster indices. These indices include the absolute size, the relative size of food industry, the level of its concentration, specialization, and market power exertion as an industrial cluster. The result shows that food industry at Gyeongsangnam-do has a potential for a regional clustering.

Deep-Learning-based Plant Anomaly Detection using a Drone (드론을 이용한 딥러닝 기반 식물 이상 탐지 시스템)

  • Lee, Jeong-Min;Lee, Yeong-Hun;Choi, Nam-Ki;Park, Heemin;Kim, Hyun-Chul
    • Journal of the Semiconductor & Display Technology
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    • v.20 no.1
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    • pp.94-98
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    • 2021
  • As the world's population grows, the food industry becomes increasingly important. Among them, agriculture is an industry that produces stocks of people all over the world, which is very important food industry. Despite the growing importance of agriculture, however, a large number of crops are lost every year due to pests and malnutrition. So, we propose a plant anomaly detection system for managing crops incorporating deep learning and drones with various possibilities. In this paper, we develop a system that analyzes images taken by drones and GPS of the drone's movement path and visually displays them on a map. Our system detects plant anomalies with 97% accuracy. The system is expected to enable efficient crop management at low cost.

A Survey of The Status of R&D Using ICT and Artificial Intelligence in Agriculture (농업에서의 ICT와 인공지능을 활용한 연구 개발 현황 조사)

  • Seonho Khang
    • Journal of the Semiconductor & Display Technology
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    • v.22 no.1
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    • pp.104-112
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    • 2023
  • Agriculture plays an industrial and economic role, as well as an environmental and ecological conservation role, group harmony and the inheritance of traditional culture. However, no matter how advanced the industry is, the basic food necessary for human life can only be produced through the photosynthesis of plants with natural resources such as the sun, water, and air. The Food and Agriculture Organization of the United Nations (FAO) predicts that the world's population will increase by another 2 billion people by 2050, and it faces a myriad of complex and diverse factors to consider, including climate change, food security concerns, and global ecosystems and political factors. In particular, in order to solve problems such as increasing productivity and production of agricultural products, improving quality, and saving energy, it is difficult to solve them with traditional farming methods. Recently, with the wind of the 4th industrial revolution, ICT convergence technology and artificial intelligence have been rapidly developing in many fields, but it is also true that the application of new technologies is somewhat delayed due to the unique characteristics of agriculture. However, in recent years, as ICT and artificial intelligence utilization technologies have been developed and applied by many researchers, a revolution is also taking place in agriculture. This paper summarizes the current state of research so far in four categories of agriculture, namely crop cultivation environment management, soil management, pest management, and irrigation management, and smart farm research data that has recently been actively developed around the world.

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