Hybrid modeling of greenhouse tomato growth using a scale-adaptive temporal convolutional network

2026-08-11

PRE

Qingzhi Liu

E. Heuvelink

Önder Babur

Yuqi Zhang

Bedir Tekinerdogan

Tao Li *

DOI：10.1016/j.compag.2026.112281

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• A novel SATCN-Informer architecture applies to greenhouse crop growth modeling. • A scale-adaptive TCN effectively models crop dynamic responses to environment. • A knowledge-data-driven training strategy balances accuracy and interpretability. • Maintains high accuracy and robustness across tomato growth stages.

Keywords:

Tomato growth modeling

Hybrid model

Deep learning

Scale-adaptive temporal features

Protected agriculture

Hybrid modeling of greenhouse tomato growth using a scale-adaptive temporal convolutional network

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