Click on “Download PDF” for the PDF version or on the title for the HTML version.


If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Development of Sensor System for the Internet of Things (IoT)-based Automated In-Field Monitoring to Support Crop Improvement Programs

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2021 ASABE Annual International Virtual Meeting  2100696.(doi:10.13031/aim.202100696)
Authors:   Worasit Sangjan, Arron Hyrum Carter, Michael O Pumphrey, Sindhuja Sankaran
Keywords:   High-throughput phenotyping, internet of things, Raspberry Pi, sensor

Abstract. Sensor applications for plant phenotyping can advance and strengthen crop improvement programs. One of the powerful sensing options is the internet of things (IoT)-based sensor technology, which can be customized and apply for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in the spring wheat breeding trial. Such an in-field sensor system will increase the reproducibility of measurements and improve selection efficiency by investigating the dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and NoIR) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are to-wards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision-making.

(Download PDF)    (Export to EndNotes)