Bilinear LSTM with Bayesian Gaussian Optimization for Predicting Tomato Plant Disease Using Meteorological Parameters

Climate change threatens agriculture; as a result, adaptation measures are required to withstand agricultural produce, reduce susceptibility, and improve the farm system's flexibility to climate change. Meteorological parameters like temperature and relative humidity play an essential role in t...

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Veröffentlicht in:	Ingénierie des systèmes d'Information 2024-04, Vol.29 (2), p.479-492
Hauptverfasser:	Wagle, Shivali Amit, Harikrishnan, R, Kotecha, Ketan
Format:	Artikel
Sprache:	eng ; fre
Schlagworte:	Agricultural production Autoregressive models Bayesian analysis Climate change Crop diseases Datasets Deep learning Humidity Infections Meteorological parameters Neural networks Optimization Pathogens Performance prediction Plant diseases Prediction models Rain Regression models Relative humidity Statistical analysis Tomatoes Weather forecasting
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