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

TL;DR

This paper introduces a novel multi-step plant growth prediction model combining wavelet decomposition, LSTM encoder-decoder, and attention mechanisms, significantly improving accuracy over existing methods.

Contribution

It presents a new multi-step prediction framework for plant growth using wavelet, LSTM, and attention, addressing error accumulation in long-term forecasts.

Findings

Outperforms existing models in RMSE, MAE, MAPE

Effectively captures long-term dependencies in plant growth data

Reduces noise and improves feature extraction through wavelet decomposition

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.