• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.385-391
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• 2018

Abalone aquacultural industry has been growing rapidly in a short period of time, however, there has been just a few researches related to the forecast of the supply, demand and price. Even the models developed by these researches have problems of low compatibility and reliability. To resolve these problem, a biological supply model needs to be developed that maintains time difference and linkage among the quantity of juvenile abalone into the plots, quantity of cultivation, quantity of shipment, and at the same time juvenile abalone is transplanted into the plot, matured and shipped by the expected market price. This study focus on the development of the model for determining quantity of the abalone cultivation, which is the core part of the entire abalone demand and supply model. Key factors that affect cultivation quantity were identified and verified the causal relationship among these variables and cultivation quantity. It turned out that the quantity of juvenile abalone transplanted and the relative price(the abalone price of the place of produce divided by the brown seaweed price) have a great influence on the cultivation quantity. Also, the similarity of the variation for the cultivation quantity of the observed value and the forecasted value implies that the model developed in this study has a high compatibility.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.