Novel hybrid ARIMA–BiLSTM model for forecasting of rice blast disease outbreaks for sustainable rice production

Abstract

In recent years, the application of artificial intelligence (AI) in agriculture has grown to be the most important research domain. The proposed work focuses on forecasting rice blast disease outbreaks in paddy crops. Disease management in the farm fields is the most difficult problem on the planet. There is a variety of reasons for this, first, a lack of farmers’ experience in diagnosing diseases, second experts’ experience in detecting diseases visually, and third unfavorable climate. In recent days, researchers have offered a variety of time-series techniques in different applications. This study adds time-series techniques to the field of agriculture by forecasting crucial rice blast disease outbreaks in the paddy crop of the Davangere region based on daily weather data obtained from KSNDMC. The statistical time-series technique called ARIMA is trained by employing real data of blast disease outbreaks in the Davangere region from the period of 2015–2019. Meanwhile, the deep BiLSTM model is trained by employing real weather data and blast disease outbreaks of the Davangere region. Both models are evaluated by performance metrics, such as mean squared error and mean absolute error. The proposed research is focused on the hybrid model ARIMA–BiLSTM which is a combination of the statistical ARIMA model and deep BiLSTM model. The seasonal component of the rice blast disease outbreak feature is extracted from the additive decompose function used in the ARIMA model and fed as a dependent feature for the BiLSTM model. According to the results obtained, the hybrid approach can successfully forecast blast disease outbreaks in paddy crops with a mean squared error of 0.037 and a mean absolute error of 0.028 compared to the statistical ARIMA and deep BiLSTM model.