ABSTRACT
Based on the analysis of crop growth cycle and water demand, the factors affecting crop growth water use are divided into three categories: environmental factors, crop factors and soil factors. The training set and test set of the model are selected from the crop irrigation historical data set that meets the expected quality and yield. By designing an intelligent farmland irrigation model based on LSTM neural network algorithm, a method of precise irrigation according to crop growth needs, growth environment and planting soil is proposed. According to the characteristics of factors affecting the water consumption for crop growth, the number of hidden layers of the prediction model is determined, and the network parameters are adjusted; The model is trained on the processed historical irrigation data set to obtain the crop irrigation volume prediction model; The LSTM neural network irrigation prediction model is compared with the traditional RNN neural network irrigation prediction model. The experimental results show that the predicted value and trend of LSTM irrigation prediction model are closer to the real value, with stronger robustness, lower error rate and shorter running time, which can meet the prediction of intelligent farmland irrigation and provide reliable basis for the research of intelligent agriculture.
- PATIL P,DESAI B L.Intelligent irrigation control system by employing wireless sensor networks.International Journal of Computer Applications,2013,79(11):33-40.Google Scholar
- Han Guili, Cai Zonghui. Design of intelligent irrigation water-saving system based on PLC and Internet of Things sensing. Agricultural Mechanization Research, 2017,39 (12): 215-218+263.Google Scholar
- Zhang Qiuyan. Design of intelligent water-saving irrigation system based on single-chip computer. Journal of Yulin University, 2017,27 (06): 31-33.Google Scholar
- Feng Chunpeng, Pan Dongling. Design of MEMS pressure sensor performance test system. Electromechanical Technology, 2016 (03): 48-50+55.Google Scholar
- HENDRAWAN Y,MURASE H.Neural-intelligent water drops algorithm to select relevant textural features for developing precision irrigation system using machine vision.Computers and Electronics in Agriculture,2011,77(2):214-228.Google Scholar
- Lin Juan, Liu Wenli. Research on Intelligent Agricultural Information System. Electrical Application, 2018,37 (24): 91-93.Google Scholar
- Feng Zhaoyu, Cui Tianshi, Zhang Zhichao, Cold rice irrigation system based on grey neural network and fuzzy control. Journal of Irrigation and Drainage, 2018,37 (04): 71-79.Google Scholar
- GIRI M,WAVHAL D N.Automated intelligent wireless drip irrigation using linear programming.International Journal of Advanced Research in Computer Engineering&Technology(IJARCET),2013,2(1):1-5.Google Scholar
- Chen Huaiyu, Yin Dayi, Zhang Quan. Analysis and verification of LSTM network to improve the positioning accuracy of MEMS inertial navigation. Chinese Journal of Inertial Technology, 2018,26 (05): 610-615.Google Scholar
- Wang Xin, Wu Ji, Liu Chao, Fault time series prediction based on LSTM recurrent neural network. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44 (04): 772-784.Google Scholar
Index Terms
- Research and Design of Intelligent Farmland Irrigation System Based on Neural Network
Recommendations
Soil moisture prediction model based on LSTM and Elman neural network
AISS '22: Proceedings of the 4th International Conference on Advanced Information Science and SystemChina is a large agricultural country, and in the process of agricultural production, it is very important to make accurate prediction of soil moisture. To address the problems of local minimization and slow convergence of traditional BP (back ...
An improved back propagation neural network prediction model for subsurface drip irrigation system
A crop yield-irrigation water model based on an improved GA-BP neural network prediction algorithm is proposed.A detailed comparison between GA-BP and normal BP model is given.The GA-BP model describes the relationship between the yield and irrigation ...
Irrigation, Drainage and Ecological Engineering Approaches to Controlled Farmland Nonpoint Source Pollution
ISDEA '13: Proceedings of the 2013 Third International Conference on Intelligent System Design and Engineering ApplicationsFarmland nonpoint source pollution (FNPSP) was considered as the chemical to river, lake or groundwater with the surface runoff, seepage or farmland drainage. It is distributed widely and scattered, and difficult to control. A framework to prevent and ...
Comments