Microclimate-Controlled Smart Growth Cabinets for High-Throughput Plant Phenotyping

Area of Science:

• Plant Science
• Agricultural Technology
• Climate Change Research

Background:

• Climate change necessitates crop varieties resilient to extreme conditions.
• Systematic plant phenotyping under controlled, dynamic environments is crucial for identifying resilient crops.
• Existing growth cabinets (GC) are limited by cost, static environments, and scalability, hindering large-scale climate change research.

Purpose of the Study:

• To introduce a novel microclimate-controlled smart growth cabinet (MCSGC) platform.
• To address the limitations of current growth cabinets in cost, scalability, and environmental dynamism.
• To facilitate advanced plant phenotyping for climate resilience research.

Main Methods:

• Development of a modular, cost-effective (<$10,000 AUD) MCSGC platform.
• Implementation of programmable environmental 'recipes' for dynamic microclimate simulation.
• Integration of interconnected, scalable multi-cabinet systems for parallel experiments.
• Automated data collection and synchronization for AI-driven analysis.

Main Results:

• Precise climate control achieved, with 97.42% of data within ±2 °C during dynamic simulation.
• Demonstrated adaptability across diverse crops including *Cannabis sativa*, *Beta vulgaris*, and *Lactuca sativa*.
• High-throughput data generation with 456 images and 164,160 sensor readings per experiment without manual intervention.
• Validation of environmental parameters suitable for broad crop cultivation (14.6-31.04 °C, 0-1241 µmol·m^-2·s^-1 PAR).

Conclusions:

• The MCSGC platform overcomes critical limitations of existing growth cabinets.
• It enables cost-effective, large-scale, dynamic climate simulations for plant phenotyping.
• The system supports advancements in crop improvement, AI applications, and climate resilience research.