Open AccessID3 algorithm and its improved algorithm in agricultural planting decision
Author(s) -
Guangping Qiang,
Lin Sun,
Qiang Huang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/474/3/032025
Subject(s) - agriculture , algorithm , computer science , data mining , foundation (evidence) , decision tree , agricultural productivity , realization (probability) , agricultural engineering , engineering , mathematics , geography , statistics , archaeology
With the advent of the era of 5G big data, it is not only necessary to mine valid data in mass information, but also to classify it. Mining and classifying information can be used as an important basis for decision making. Making correct and efficient decisions on agricultural planting can not only improve agricultural production efficiency, but also lay the foundation for the realization of smart agriculture. This paper will compare the improved algorithm, which is called MIND, with the algorithms in other literaturesby experiments. Through calculations in agricultural big data such as temperature, humidity, wind, and weather, it is demonstrated that the improved algorithm is more suitable for agricultural planting decisions.