Affordable Remote Monitoring of Plant Growth and Facilities using Raspberry Pi Computers

Premise of the study: Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated remote monitoring of such facilities with low-cost hardware can greatly improve both the reproducibility and the accurate maintenance of their conditions. Methods and Results: Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed a cost-effective system for monitoring growth chamber conditions, which we have called ‘GMpi.’ Coupled with our software, GMpi_Pack, our setup automates sensor readings, photography, alerts when conditions fall out of range, and data transfer to cloud storage services. Conclusions: The GMpi offers low-cost access to environmental data logging, improving reproducibility of experiments, as well as reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing customization and expansion to include other features such as machine vision.

[1]  J. O. Rawlings,et al.  Design of Experiments in Growth Chambers — Uniformity Trials in the North Carolina State University Phytotron1 , 1982 .

[2]  Xinwei Deng,et al.  Experimental design , 2012, WIREs Data Mining Knowl. Discov..

[3]  Xinrong Li,et al.  Wireless Sensor Network System Design Using Raspberry Pi and Arduino for Environmental Monitoring Applications , 2014, FNC/MobiSPC.

[4]  Malia A. Gehan,et al.  A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria. , 2015, Molecular plant.

[5]  Mark Ungrin,et al.  A Simple and Low-Cost Monitoring System to Investigate Environmental Conditions in a Biological Research Laboratory , 2016, PloS one.

[6]  Noah Fahlgren,et al.  Naïve Bayes pixel-level plant segmentation , 2016, 2016 IEEE Western New York Image and Signal Processing Workshop (WNYISPW).

[7]  Febus Reidj G. Cruz,et al.  Wireless sensor network for agricultural environment using raspberry pi based sensor nodes , 2017, 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[8]  Neel Pradip Shah,et al.  GREENHOUSE AUTOMATION AND MONITORING SYSTEM DESIGN AND IMPLEMENTATION , 2017 .

[9]  Andy Lin,et al.  PlantCV v2: Image analysis software for high-throughput plant phenotyping , 2017, PeerJ.

[10]  Jeffrey C. Berry,et al.  An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping , 2018, bioRxiv.