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 Collection: Gathering weather, soil, crop yield, and GIS data from reliable sources. Data Preprocessing: Cleaning, normalizing, and integrating data for effective analysis. Model Development: ...

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 ...

Technology adoption can address these issues, improving production and quality. Machine learning, a subset of Artificial Intelligence (AI), enables prediction, classification, and automation in ...

The timely and accurate prediction of maize (Zea mays L.) yields prior to harvest is critical for food security and agricultural policy development. Currently, many researchers are using machine ...

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.

Accurately predicting crop yields is essential for mitigating these risks and providing information on sustainable agricultural practices. This research presents a novel crop yield prediction system ...

Ensemble algorithms demonstrated superior performance in crop yield prediction (Ahmed, 2023), while RF is the optimized algorithm for accurately forecasting maize yields at the county level through ...