Open AccessStudy on soft sensing method of plant growth water demand information based on RBF neural network
Author(s) -
Meiguang Li,
Lei Tian,
J. Y. Zhang,
Huiming Duan
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/594/1/012007
Subject(s) - plant growth , artificial neural network , computer science , process (computing) , soft computing , agricultural engineering , artificial intelligence , data mining , engineering , agronomy , operating system , biology
As one of the important factors in the process plant growth and development, water plays a very important role. Plant water potential is one of the important indexes to characterize plant water physiological characteristics. To obtain the accurate plant water potential, and then get the plant growth water demand information, can better guide the plant precision irrigation. Based on the soft sensing and artificial intelligence technology, the soft sensing model of plant growth water demand information based on RBF neural network is established and simulated. The simulation results show that the method has a high estimation accuracy and can measure and predict the water demand information of plant growth.