Real-Time Rain Prediction in Agriculture using AI and IoT: A Bi-Directional LSTM Approach - Peeriga, Radhika; Rinku, Dhruva R.; Bhaskar, J. Uday; Nagalingam, Rajeswaran; Aldosari, Fahd M.; Albarakati, Hussain M.; Alharbi, Ayman A.; Jaffar, Amar Y. (Engineering, Technology & Applied Science Research, 2024)
Accurate rain forecasting is crucial for optimizing agricultural practices and improving crop yields. This study presents a real-time rain forecasting model using a Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm for an on-device AI platform. The model uses historical weather data to predict rainfall, enabling farmers to make data-driven decisions in irrigation, pest control, and field operations. This model enables farmers to optimize water use, conserve energy, and improve overall resource management. Real-time capabilities allow immediate adjustments to agricultural activities, mitigating risks associated with unexpected weather changes. The Bi-LSTM model achieved a mean accuracy of 92%, significantly outperforming the traditional LSTM (85%) and ARIMA (80%) models. This high accuracy is attributed to the model's bidirectional processing capability, which captures comprehensive temporal patterns in the weather data. Implementing this model can enhance decision-making processes for farmers, resulting in increased productivity and profitability in the agricultural sector.