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

被引:0
|
作者
M. Varsha
B. Poornima
M. P. Pavan Kumar
S. Basavarajappa
机构
[1] Bapuji Institute of Engineering and Technology,
[2] Jawaharlal Nehru National College of Engineering,undefined
[3] G. M. Institute of Technology,undefined
关键词
Statistical ARIMA; Autocorrelation; Partial autocorrelation; BiLSTM; Novel hybrid ARIMA–BiLSTM;
D O I
10.1007/s42044-022-00128-3
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:147 / 159
页数:12
相关论文
共 50 条
  • [2] TESTING A COMPUTERIZED FORECASTING SYSTEM FOR RICE BLAST DISEASE
    KIM, CH
    MACKENZIE, DR
    RUSH, MC
    PHYTOPATHOLOGY, 1985, 75 (11) : 1319 - 1319
  • [3] Antioxidant-mediated suppression of ferroptosis in Pyricularia oryzae: a novel approach to rice blast management for sustainable rice production
    Santoni, Mattia
    Molina-Hernandez, Junior Bernardo
    Kunova, Andrea
    Cortesi, Paolo
    Brunetti, Barbara
    Rocculi, Pietro
    Christodoulou, Michael S.
    Danesi, Francesca
    FRONTIERS IN PLANT SCIENCE, 2024, 15
  • [4] FIELD TESTING A COMPUTERIZED FORECASTING SYSTEM FOR RICE BLAST DISEASE
    KIM, CH
    MACKENZIE, DR
    RUSH, MC
    PHYTOPATHOLOGY, 1988, 78 (07) : 931 - 934
  • [5] Computer simulation approaches for rice blast disease forecasting in Japan
    Nemoto, F
    Ishiguro, K
    RICE BLAST: INTERACTION WITH RICE AND CONTROL, 2004, : 289 - 295
  • [6] The Control of Rice Blast Disease by the Novel Biofungicide Formulations
    Chen, Wen-Ching
    Chiou, Tai-Ying
    Delgado, Aileen L.
    Liao, Chien-Sen
    SUSTAINABILITY, 2019, 11 (12)
  • [7] A possible mechanism for breakdown of resistance in hybrid rice to blast disease
    Peng, YL
    Chen, GH
    He, M
    50TH INTERNATIONAL SYMPOSIUM ON CROP PROTECTION, PTS I-IV, 1998, 50 : 947 - 956
  • [8] An extension of mathematical model for severity of rice blast disease
    Tabonglek, Saharat
    Khan, Amir
    Humphries, Usa Wannasingha
    AIMS MATHEMATICS, 2023, 8 (01): : 2419 - 2434
  • [9] A hybrid machine model of rice blast fungus, Magnaporthe grisea
    Holcombe, M
    Holcombe, L
    Gheorghe, M
    Talbot, N
    BIOSYSTEMS, 2003, 68 (2-3) : 223 - 228
  • [10] Allelic variation in rice blast resistance: a pathway to sustainable disease management
    Younas, Muhammad Usama
    Qasim, Muhammad
    Ahmad, Irshad
    Feng, Zhiming
    Iqbal, Rashid
    Abdelbacki, Ashraf M. M.
    Rajput, Nimra
    Jiang, Xiaohong
    Rao, Bisma
    Zuo, Shimin
    MOLECULAR BIOLOGY REPORTS, 2024, 51 (01)