AI in Agriculture - Episode 8: Weather Forecasting with Data-Driven Models for Better Irrigation & Yield
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AI in Agriculture - Episode 8: Weather Forecasting with Data-Driven Models for Better Irrigation & Yield
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In this episode, we dive into how advanced weather forecasting models are transforming precision agriculture. From improving water use to boosting crop yields, cutting-edge models like Temporal Convolutional Neural Networks (TCNN) and CatBoost are helping farmers make smarter decisions.
🔍 Key takeaways:
- 140,160 agrometeorological data points from Morocco's Sidi Rahal region were used to build forecasting models based on air temperature, solar radiation, and relative humidity.
- The TCNN model achieved remarkable accuracy, with an RMSE of 0.88°C for 1-day air temperature forecasts, outperforming traditional models.
- For solar radiation, TCNN achieved an RMSE of 29.26 W/m², allowing farmers to fine-tune irrigation schedules and conserve water.
- CatBoost excelled in longer forecasts, with an RMSE of 25.20 W/m² for 1-week solar radiation predictions, showing how machine learning can improve farming efficiency.
🌦 Why it matters:
Farmers can now plan irrigation with 1-day to 3-day precision, saving water and reducing costs in regions with limited resources.
Accurate forecasts prevent crop losses and boost yields by responding quickly to changing weather conditions.
💡 What you'll learn:
- How data-driven models like TCNN and CatBoost are shaping the future of agriculture.
- The critical role of weather forecasting in improving resource management and sustainability on farms.
- The impact of AI and machine learning on adapting agriculture to climate change and resource scarcity.
- Join us to explore the future of farming, where weather tech meets AI innovation! 🎙️🌍
🔔 Subscribe now to stay updated on how AI is revolutionizing agriculture!
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