HINTS, an LSTM that maps partial growth and environmental observations to parameters of a softplus growth curve, forecasts harvest height and mass up to five days ahead with lower mean absolute error than rolling-average baselines.
Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives
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Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data
HINTS, an LSTM that maps partial growth and environmental observations to parameters of a softplus growth curve, forecasts harvest height and mass up to five days ahead with lower mean absolute error than rolling-average baselines.