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CBM：一种用于田间作物高度和生物量测量的物联网支持的激光雷达传感器。

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

机构信息

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

Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia.

出版信息

Biosensors (Basel). 2021 Dec 29;12(1):16. doi: 10.3390/bios12010016.

DOI:10.3390/bios12010016
PMID:35049643
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8774002/
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.

摘要

作物基因型的表型特征是作物管理和农业研究中必不可少但具有挑战性的方面。数字传感技术正在快速推进植物表型分析，并加速作物育种的成果。然而，由于作物种类的多样性和植物育种选择过程中的特殊需求，现成的传感器可能不完全适用和适合农业研究。具有专用传感器硬件和软件架构的定制传感系统提供了强大且低成本的解决方案。本研究设计和开发了一种完全集成的基于 Raspberry Pi 的激光雷达传感器，名为 CropBioMass (CBM)，通过物联网实现，提供完整的端到端管道。CBM 是一种低成本传感器，可在现场提供高通量无缝数据采集、小数据占用空间、将数据注入远程服务器以及自动化数据处理。CBM 数据估算的作物鲜生物质、干生物质和株高的表型特征与小麦田间试验中的地面真实手动测量高度相关。CBM 可方便地应用于高通量植物表型分析、作物监测和管理，以实现精准农业应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/2fba33ae16ab/biosensors-12-00016-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/fb190c733d80/biosensors-12-00016-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/3307af0cd5c3/biosensors-12-00016-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/40f76301c03d/biosensors-12-00016-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/9877d93349fd/biosensors-12-00016-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/2fba33ae16ab/biosensors-12-00016-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/28f4936331e8/biosensors-12-00016-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/fb190c733d80/biosensors-12-00016-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/649a549ab81a/biosensors-12-00016-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/36888f73da27/biosensors-12-00016-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/eb578b6bc3bc/biosensors-12-00016-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/d3398f1f9317/biosensors-12-00016-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/3307af0cd5c3/biosensors-12-00016-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/40f76301c03d/biosensors-12-00016-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/9877d93349fd/biosensors-12-00016-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d619/8774002/2fba33ae16ab/biosensors-12-00016-g010.jpg

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