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Research and Design of Intelligent Farmland Irrigation System Based on Neural Network

Published: 26 June 2023 Publication History

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.

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  1. Research and Design of Intelligent Farmland Irrigation System Based on Neural Network

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      IEEA '23: Proceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications
      February 2023
      97 pages
      ISBN:9798400700125
      DOI:10.1145/3594692
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 June 2023

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      Author Tags

      1. Intelligent farmland
      2. LSTM
      3. Neural Network

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      • Research-article
      • Research
      • Refereed limited

      Funding Sources

      • The National key research and development plan in 2022: The critical technology and demonstration for barrier horizon reduction and productivity improvement of the Albic soil in the Sanjiang Plain
      • The Strategic Priority Research Program of the Chinese Academy of Sciences
      • Heilongjiang Postdoctoral Foundation Project in 2022: The formation mechanism of soil organic carbon and improvement effect driven by straw returning for Albic soil in Sanjiang Plain

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