An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
This work provides an improved multi-step prediction method for plant growth, which is beneficial for agricultural planning and management.
This paper addresses the challenge of multi-step plant growth prediction, specifically for Stem Diameter Variations (SDV), which is difficult due to error accumulation in existing one-step-ahead forecasting methods. The proposed approach significantly outperforms existing models in terms of RMSE, MAE, and MAPE.
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.