Real-Time Rain Prediction in Agriculture using AI and IoT: A Bi-Directional LSTM Approach
Received: 3 June 2024 | Revised: 16 June 2024 | Accepted: 20 June 2024 | Online: 25 June 2024
Corresponding author: Rajeswaran Nagalingam
Abstract
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.
Keywords:
AI, IoT, Bi-LSTM, rain prediction, precision farming, smart agriculture, weather forecastingDownloads
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Copyright (c) 2024 Radhika Peeriga, Dhruva R. Rinku, J. Uday Bhaskar, Nagalingam Rajeswaran, Fahd M. Aldosari, Hussain M. Albarakati, Ayman A. Alharbi, Amar Y. Jaffar
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