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CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements.

Bikram Pratap Banerjee1, German Spangenberg2,3, Surya Kant1,2,3

  • 1Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia.

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|January 20, 2022
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Summary
This summary is machine-generated.

A new low-cost Raspberry Pi-based LiDAR sensor, CropBioMass (CBM), enables high-throughput plant phenotyping. This system accurately measures crop biomass and height, accelerating crop breeding and precision agriculture.

Keywords:
GNSSLiDARRaspberry Pihigh-throughput plant phenotypinginternet of thingsprecision agriculture

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Area of Science:

  • Agricultural Science
  • Sensor Technology
  • Plant Breeding

Background:

  • Accurate crop genotype characterization is crucial for agriculture research and management.
  • Digital sensing technologies accelerate plant phenotyping but off-the-shelf sensors often lack crop-specific suitability.
  • Customized sensing systems offer a powerful, low-cost solution for diverse agricultural needs.

Purpose of the Study:

  • To design and develop an integrated, low-cost Raspberry Pi-based LiDAR sensor system for plant phenotyping.
  • To create a complete end-to-end pipeline for high-throughput data collection and processing.
  • To validate the sensor's accuracy in estimating key crop phenotypic traits.

Main Methods:

  • Development of a Raspberry Pi-based LiDAR sensor system named CropBioMass (CBM).
  • Integration with Internet of Things (IoT) for seamless data collection and remote server injection.
  • Automated data processing for phenotypic trait estimation.

Main Results:

  • The CropBioMass (CBM) sensor demonstrated high-throughput, seamless field data collection with a small data footprint.
  • Phenotypic traits including crop fresh biomass, dry biomass, and plant height were accurately estimated.
  • CBM-estimated traits showed a high correlation with ground truth manual measurements in a wheat field trial.

Conclusions:

  • The CropBioMass (CBM) sensor provides a low-cost, effective solution for high-throughput plant phenotyping.
  • The system is readily applicable for crop monitoring, management, and precision agriculture.
  • This technology accelerates crop breeding outcomes through efficient data acquisition and analysis.