Diel phenotyping platform for optimising crop harvest timing through circadian growth analysis

Overview

This solution offers a phenotyping platform that measures circadian rhythms of plant growth to identify the optimal time to harvest or cull crops. The technology is particularly relevant for crops and decorative vegetation where growth is the primary measure of yield, such as rye grass and other monocots. By characterising when growth occurs during the 24-hour cycle, growers and breeders can align agricultural practices with the plant's natural growth windows, potentially improving yield and resource efficiency.

The platform addresses a key gap in agricultural phenotyping: most growth measurements capture total biomass over days or weeks, missing the temporal dynamics within each day. The research has demonstrated that monocots and dicots exhibit fundamentally different growth timing, with wheat and Brassicas showing distinct diel patterns. This inter-species variation can be exploited to maximise agricultural outcomes.

Technical specifications

Key features:

• A robust phenotypic assay for circadian rhythms of growth in plant seedlings
• Imaging platform built on single-board computers with infrared imaging infrastructure
• Machine learning-based image analysis pipeline for characterising diel growth patterns
• Ability to measure growth in both long-day and short-day photoperiods
• Validated across multiple species including Arabidopsis, wheat, and Brassica
• Applicable to both monocots and dicots, with demonstrated variety-specific growth patterns

The system works by continuously imaging seedlings under infrared light, which allows growth to be tracked without disrupting the plant's natural light cycle. The resulting time-series data is processed through a machine learning pipeline that extracts precise timing metrics for growth phases. This enables researchers to identify the specific windows during which different varieties accumulate biomass most rapidly.

Technology readiness level

The platform has been validated in laboratory settings across multiple plant species, including Arabidopsis, wheat, and Brassica magic populations. It has demonstrated consistent variety-specific growth patterns and has been adapted from zebrafish imaging applications, showing cross-domain robustness. The current stage involves applying the technique to commercial crop varieties, with a future goal of conducting a proof-of-concept field plot trial for a grass variety to demonstrate chrono-culture utility in real agricultural conditions.