머신러닝 적용 과일 수확시기 예측시스템 설계 및 구현

Abstract

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 studyhow to apply machine learning techniques to foods, which have the greatest influence on the human survival. Inthe field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus onoptimizing the crop growth environment by monitoring the growth environment in real time. KT Smart FarmSolution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existingsmart farm businesses mainly focus on controlling the growth environment and improving productivity. On theother hand, in this study, we are studying how to apply machine learning with respect to harvest time so that wewill be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machinelearning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, toapply 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 usingcolor, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be usedrepeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulatedcontinuously.