Hybrid ML framework forecasts cattle weight gain in grazing systems

Researchers have developed a hybrid machine learning framework to forecast cattle weight gain and growth patterns in grazing systems. The framework integrates various sensing data, including live weight, demographics, and environmental factors, to predict herd-level trajectories. The cascade architecture combining Gradient Boosting, Random Forest, and Neural Networks demonstrated superior performance, achieving an R^2 of 0.889 and outperforming traditional recurrent models, especially with sparse data. AI

IMPACT This framework could improve livestock management decisions by providing more accurate weight gain forecasts.

RANK_REASON Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• ARIMA
• Neural Network
• arXiv
• Australia
• Gradient Boosting
• LSTM
• Muhammad Riaz Hasib Hossain
• Random Forest

• paper
• product

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