| Water quantity prediction of regulated deficit irrigation for green peppers based on deep learning artificial neural network |
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| DOI：10.7606/j.issn.1000-7601.2021.06.12 |
| Key Words: regulated deficit irrigation drip irrigation artificial neural network deep learning green peppers prediction |
| Author Name | Affiliation | | LIU Jingran | Hydropower College, Hebei University of Engineering, Handan, Hebei 056038, China
Hebei Key Laboratory of Intelligent Water Conservancy, Handan, Hebei 056038, China | | WU Haixia | Hydropower College, Hebei University of Engineering, Handan, Hebei 056038, China
Hebei Key Laboratory of Intelligent Water Conservancy, Handan, Hebei 056038, China | | LIU Xin | School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China | | LIU Zhen | School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China | | WANG Pengyu | School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China | | ZHANG Youqiang | School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China | | LI Yuqiong | School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China |
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| Abstract: |
| A combination of ridge and furrow rain\|collecting and rain\|covering planting drip\|irrigation technology and regulated deficit irrigation technology (MFR-RDI) was adopted from 2014 to 2018 to carry out an experimental study on green peppers. The experimental treatment with the highest utilization efficiency of irrigation water (severe deficit water in the later period of green peppers results) was selected to predict the irrigation water amount. Based on the data collected during the experiment, the deep learning artificial neural network (DNN) prediction model of green pepper crop irrigation water amount was established under the MFR-RDI planting method. The model took crop water requirement, growth period of green pepper, precipitation, soil water content and irrigation amount of the previous day as input factors. Through model test, the best DNN prediction model was obtained. The hidden layer of the model included 4 layers, and the number of neurons in each hidden layer was 32, 16, 8, and 4, respectively. The activation function of the model was “ReLU”, the optimization function was “adam”, and the number of iterations was 300. The model was tested using data from 2018. The test results showed that the RMSE of DNN model was 0.898 mm, MAE was 0.257 mm, NS was 0.758, and R2 was 0.7635, indicatinga high accuracy performance of the prediction model. Through the prediction results, the irrigation system of green peppers under this planting method can be obtained, providing reference for realization of efficient and intelligent water\|saving irrigation. |
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