Abstract
The 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 agricultural research due to the diversity in crop species and specific needs 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 remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.
Highlights
Crop biomass and height are fundamental morphological traits to estimate crop growth and selection of genotypes of interest in a breeding program
The system development incorporated the integration of a Raspberry Pi 4 board as the central processing unit, LeddarTech Vu8 module as the light detection and ranging (LiDAR) unit, and Navio2 as the global navigation satellite system (GNSS) unit
This study reports the development of CBM, a low-cost integrated sensor system with LiDAR to measure crop biomass and height in the field
Summary
Crop biomass and height are fundamental morphological traits to estimate crop growth and selection of genotypes of interest in a breeding program. Crop biomass is associated with plant growth and development, being the basis of vigor and net primary productivity [1,2,3]. Plant height is the vertical distance from the ground level to the upper boundary of the primary photosynthetic tissues [5,6] and is conventionally measured in the field using rulers. These manual and destructive data collection methods are inefficient, laborious, expensive, prone to manual error, not repeatable, and only applicable to small scale field trials. Proximal digital sensing technologies overcome such challenges by offering a practical solution for high-throughput plant phenotyping (HTPP) of crop biomass and height [7,8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
