AI in Agriculture

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:


  1. How data-driven models like TCNN and CatBoost are shaping the future of agriculture.
  2. The critical role of weather forecasting in improving resource management and sustainability on farms.
  3. The impact of AI and machine learning on adapting agriculture to climate change and resource scarcity.
  4. 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!




Any questions or ideas?

Drop us an email to [email protected]


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AI in AgricultureBy Maryna Kuzmenko