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http://dx.doi.org/10.3837/tiis.2019.06.009

A Water-saving Irrigation Decision-making Model for Greenhouse Tomatoes based on Genetic Optimization T-S Fuzzy Neural Network  

Chen, Zhili (College of Information and Electrical Engineering, China Agricultural University)
Zhao, Chunjiang (National Engineering Research Center for Information Technology in Agriculture)
Wu, Huarui (National Engineering Research Center for Information Technology in Agriculture)
Miao, Yisheng (National Engineering Research Center for Information Technology in Agriculture)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.6, 2019 , pp. 2925-2948 More about this Journal
Abstract
In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.
Keywords
T-S fuzzy neural network; genetic optimization; krill herd; irrigation decision; greenhouse tomatoes; Internet of Things;
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