CBM: An IoT enabled LiDAR sensor for in-field crop height and biomass
measurements
Abstract
Phenotypic characterization of crop genotypes is an essential yet
challenging aspect of crop management and agriculture research. Digital
sensing technologies are rapidly advancing plant phenotyping and
speeding-up crop breeding outcomes. However, off-the-shelf sensors might
not be fully applicable and suitable for agriculture research due to
diversity in crop species and specific need during plant breeding
selections. Customized sensing systems with specialized sensor hardware
and software architecture provide a powerful and low-cost solution. This
study designed and developed a fully integrated Raspberry Pi-based LiDAR
sensor named CropBioMass (CBM), enabled by internet of things to provide
a complete end-to-end pipeline. The CBM is a low-cost sensor, provides
high-throughput seamless data collection in field, small data footprint,
injection of data onto the re-mote server, and automated data
processing. Phenotypic traits of crop fresh biomass, dry biomass and
plant height estimated by CBM data had high correlation with ground
truth manual measurements in wheat field trial. The CBM is readily
applicable for high-throughput plant phenotyping, crop monitoring and
management for precision agricultural applications.