Crop Growth Stage Prediction

The Technology

Accurately estimating short-interval arable crop growth stages can be achieved combining local daily air temperature data known as active accumulated temperatures (AAT) and crop biophysical properties such as leaf area index (LAI), normalised difference vegetation index (NDVI), enhanced vegetation index (EVI) and green chlorophyll vegetation index (GCVI). All of these can be estimated or monitored from orbiting satellites using synthetic aperture radar (SAR) or optical sensors.

One of the problems for early growth stage estimation in UK arable crops is highly variable regional weather patterns generating interseason and field-to-field variation for data-mining, and changeable variety profile. This may be overcome to some extent by using growth stages estimated from active accumulated temperature (AAT) data to align NDVI time series data.

How it Works

Crop growth stage prediction tools are most accurate when they integrate machine deep learning techniques with temporal SAR and optical satellite data, precision weather data especially ‘day degrees’ and crop physiological data to understand crop growth and its relationship with Leaf Area Index (LAI), Biomass and Growth Stages. There are currently no remote-sensed ‘in field’ growth stage predictive tools for UK use, although Crop Monitor Pro uses reference crops to model local growth stages.
NOTE: Heavy duty drones with simple ‘live feed’ monitors are valuable for covering large farm areas.

Farmer / Agronomist benefits

Field-level knowledge of crop growth stages is essential for accurate input timing.

Key researchers & Stakeholders

Beijing University




Video / Social Media links


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