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Multivariate Multi-Step Agrometeorological Forecast Model for Rapid Spray

文献类型: 外文期刊

作者: Shi, Guobin 1 ; Wang, Chun 1 ;

作者机构: 1.Heilongjiang Bayi Agr Univ, Coll Engn, Daqing 163319, Peoples R China

2.Guangdong Univ Petrochem Technol, Coll Comp Sci, Maoming 525000, Peoples R China

3.Chinese Acad Trop Agr Sci, South Subtrop Crops Res Inst, Natl Soil Qual Zhanjiang Observat & Expt Stn, Zhanjiang 524003, Peoples R China

关键词: Predictive models; Data models; Convolutional neural networks; Wind speed; Spraying; Temperature distribution; Wind forecasting; ConvLSTM-EA; multi-step; multivariate output; walk-forward validation

期刊名称:IEEE ACCESS ( 影响因子:3.476; 五年影响因子:3.758 )

ISSN: 2169-3536

年卷期: 2021 年 9 卷

页码:

收录情况: SCI

摘要: The timing of spray application plays an essential role in daily pesticides management. Proper wind speed, air temperature, and relative humidity are the main external factors to improve the efficacy of pesticides, reduce the amount of spray drift and environmental pollution. Very few previous studies have focused on the need for short-term weather prediction in rapid spraying decisions. In this paper, a Convolutional-LSTM encoder-decoder (ConvLSTM-AE) hybrid model for multivariate output and multi-step prediction with short time intervals is proposed to predict these three agrometeorological variables in advance. This model can predict daily weather conditions at 15-minute interval and track the changes of time-varying systems effectively. This method was also compared with other methods such as CNN, multi-head CNN, LSTM encoder-decoder, and CNN-LSTM encoder-decoder models. The results show that the proposed model outperforms other models and is suitable for daily weather forecasts in a short time interval. The obtained rapid and accurate prediction results provide a reliable basis for precise spray timing in actual farming.

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