Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Wanhyun Cho,Myunghwan Na,Sangkyuoon Kim,Inseop Na

doi:10.3390/electronics10131576

Abstract

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

Highlights

As the amount grown in fields decreases, tomatoes, which are one of Korea’s favorite food items, used additives in diverse foods, are grown in green houses in Korea instead
Pham et al [4] present an improvement of hybrid of nonlinear autoregressive with exogeneous input (NARX) model and autoregressive moving average (ARMA) model for long-term machine state forecasting based on vibration data
From the above two analysis results, we conclude that the internal temperature, CO2 and internal humidity are the environmental factors that have a great influence on the tomato yield

Summary

Introduction

As the amount grown in fields decreases, tomatoes, which are one of Korea’s favorite food items, used additives in diverse foods, are grown in green houses in Korea instead. Several papers have recently been published that analyze the NARX time series data using deep learning networks such as RNNs or Encoder-Decoder attention model. From the overall hidden states of the recurrent layer, they derive variable specific hidden representations over time, which can be flexibility used for g-forecasting and temporal-variable level attentions In his master’s thesis, Lee [14] and Na et al [15,16] proposed a bidirectional Encoder-Decoder with dual-stage attention model that slightly modified a dual-stage attention-based recurrent neural network proposed by Qin and colleagues for multivariate time series prediction. The model checking is the testing whether the obtained model conforms to the specifications of a stationary univariate process

Hybrid Forecasting System Using Attention-Based LSTM Network and ARMA Model

33. EExxppeerriimmeennttaall RReessuullttss

Association Analysis

Prediction by Attention-Based LSTM

Prediction by Hybrid Methods

Conclusions