An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth

• URL: http://arxiv.org/abs/2012.04041v1
• Date: Mon, 7 Dec 2020 20:30:39 GMT
• Title: An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
• Authors: Bashar Alhnaity, Stefanos Kollias, Georgios Leontidis, Shouyong Jiang, Bert Schamp, Simon Pearson
• Abstract summary: This paper presents a novel approach for predicting plant growth in agriculture, focusing on prediction of plant Stem Diameter Variations (SDV) Wavelet decomposition is applied to the original data, as to facilitate model fitting and reduce noise in them. An encoder-decoder framework is developed using Long Short Term Memory (LSTM) and used for appropriate feature extraction from the data. A recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data.
• Score: 4.077787659104315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of prediction errors. This paper presents a novel approach for predicting plant growth in agriculture, focusing on prediction of plant Stem Diameter Variations (SDV). The proposed approach consists of three main steps. At first, wavelet decomposition is applied to the original data, as to facilitate model fitting and reduce noise in them. Then an encoder-decoder framework is developed using Long Short Term Memory (LSTM) and used for appropriate feature extraction from the data. Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data. Experimental results are presented which illustrate the good performance of the proposed approach and that it significantly outperforms the existing models, in terms of error criteria such as RMSE, MAE and MAPE.

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