Who needs rewrites? This metadata-powered architecture fuses AI and ETL so smoothly, it turns pipelines into self-evolving ...

Accurate crop yield prediction is critical for global food security, economic planning, and insurance modeling. Traditional process-based (PB) models rely on biophysical equations and empirical data, ...

Data Preprocessing: Cleaning, normalizing, and integrating data for effective analysis. Model Development: Employing machine learning models (e.g., LSTM, ARIMA, SVM) for weather prediction, crop ...

Crop Yield Prediction Using Federated Learning This is the official documentation of the code repository of the paper: Patrick Killeen, Iluju Kiringa, and Tet Yeap "UAV Imagery-Based Yield Prediction ...

A new machine-learning model for predicting crop yield using environmental data and genetic information can be used to develop new, higher-performing crop varieties.

Crop Yield Prediction: Using Models to Forecast Crop Yields The Crop Yield Prediction pattern leverages machine learning (ML) to forecast future agricultural crop yields. This pattern is critical in ...

We are all aware of India's reliance on agriculture. The user may forecast the agricultural production in any year they choose using the script's simple criteria, which include State, district, season ...

Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model ...