Crop Pests Prediction Method using Regression and Machine Learning Technology: Survey

被引：22

作者：

Kim, Yun Hwan ^{[1
]}

Yoo, Seong Joon ^{[1
]}

Gu, Yeong Hyeon ^{[1
]}

Lim, Jin Hee ^{[2
]}

Han, Dongil ^{[1
]}

Baik, Sung Wook ^{[3
]}

机构：

[1] Sejong Univ, Dept Comp Engn, 98 Gunja Dong Gwangjin Gu, Seoul 143747, South Korea

[2] Sejong Univ, Dept Bioresource Engn, Seoul 143747, South Korea

[3] Sejong Univ, Dept Digital Contents Engn, Seoul 143747, South Korea

来源：

2013 INTERNATIONAL CONFERENCE ON FUTURE SOFTWARE ENGINEERING AND MULTIMEDIA ENGINEERING (ICFM 2013) | 2014年 / 6卷

关键词：

Regression; Machine Learning Technology; SVM; FUSARIUM HEAD BLIGHT; DEOXYNIVALENOL CONTENT; NEURAL-NETWORK; MODEL;

D O I：

10.1016/j.ieri.2014.03.009

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper describes current trends in the prediction of crop pests using machine learning technology. With the advent of data mining, the field of agriculture is also focused on it. Currently, various studies, domestic and overseas, are under progress using machine learning technology, and cases of its utilization are increasing. This paper classifies and introduces SVM (Support Vector Machine), Multiple Linear Regression, Neural Network, and Bayesian Network based techniques, and describes some cases of their utilization. (C) 2014. The Authors. Published by Elsevier B.V.

引用

页码：52 / 56

页数：5

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