AI & ML
Towards Accessible, Autonomous Photosynthesis Monitoring on Living Leaves
arXiv cs.HC0 views
arXiv:2611.13387v1 Announce Type: new Abstract: Photosynthesis is a direct biomarker of plant health and functioning. However, measuring net photosynthesis on individual leaves requires expensive (USD 42,000 - USD 89,000) instruments and manual effort. This limits access to photosynthesis sensing and makes long-term monitoring across multiple leaves difficult. We present a 1000x low-cost, autonomous sensing system that enables longitudinal monitoring of photosynthesis on living leaves over multiple days. Guided by a formative user study with 13 plant biology researchers, the system integrates an automated, leaf-attached chamber with a low-cost CO2 sensor. Built using a 3D-printed clamp and thermoformed dome, the chamber periodically closes to measure CO2 drawdown and reopens to refresh the air. Each chamber costs about USD 62 and operates independently, allowing multiple leaves to be monitored simultaneously without pumps or gas tubing. We characterize each chamber and validate its measurements against a research-grade gas exchange instrument (CIRAS-4) across 29 sessions (R^2 = 0.90). We also demonstrate monitoring of C3 and CAM photosynthesis plants, adaptability to different leaf geometries, and a light-response experiment. In a nine-day deployment, six chambers captured simultaneous CO2 exchange from individual leaves of the same plant during drought and rewatering. Each leaf showed different patterns of nighttime CO2 uptake and recovery, which a whole-plant CO2 measurement instrument would obscure. To our knowledge, this is the first demonstration of such leaf-level photosynthesis dynamics using independent, low-cost autonomous chambers. Our work opens new opportunities for plant science, education, and data-driven agriculture.
Source:https://arxiv.org/abs/2611.13387PreviousDesigning Accessible and Rigorous ML Experiments: No-Code Visual Workflows and Structured Guidance for BeginnersNextEJAI: Judgment-Preserving Civic AI for Data Center Environmental Justice
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