An AI framework introduces InComfort-Index and EASI-Index, then uses XGBoost to predict cattle feed intake at animal level (RMSE 1.38 kg/day) and pen level (RMSE 0.14 kg/day-animal) from 16.5 million samples across 19 experiments.
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This design-science study proposes a layered architecture, closed-loop decision process, function-pain-point matrix, evaluation indicators and governance framework for the Zhinong AI agricultural decision-support platform without providing measured field performance.
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AI-based framework to predict animal and pen feed intake in feedlot beef cattle
An AI framework introduces InComfort-Index and EASI-Index, then uses XGBoost to predict cattle feed intake at animal level (RMSE 1.38 kg/day) and pen level (RMSE 0.14 kg/day-animal) from 16.5 million samples across 19 experiments.
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Zhinong AI: A Design-Science Study of an AI-Enabled Agricultural Decision-Support Platform for Smallholder Production
This design-science study proposes a layered architecture, closed-loop decision process, function-pain-point matrix, evaluation indicators and governance framework for the Zhinong AI agricultural decision-support platform without providing measured field performance.