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新型传感器和数据驱动方法-通往下一代植物表型组学新途径

发表时间:2019-10-08 14:05:14点击:37

来源:北京博普特科技有限公司

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热点

本文重点介绍了未来高通量、无损、性价比高的植物性状测量策略。在植物表型组学领域使用低成本、DIY方法为快速原型开发以及传感器科研提供了机会;稳健规程、数据优化以及来源对表型数据再次应用以及交叉验证至关重要;地下部表型研究是一个主要瓶颈,需要对根部相关特征进行研究的新技术出现。

摘要

2016年,在墨西哥CIMMYT,IPPN主办的第四届国际植物表型会议上召开了工作组会议,探讨表型传感器进展。田间应用数量的不断增多提供了新挑战,需要专业化的解决方案。

很多性状对植物生长和发育非常关键,对表型研究方法提出苛刻要求,现有方法还在初始研究阶段或无法满足当前要求。另外,当前对低成本传感器解决方案有不断增长需求,要求移动平台可运输到实验场地,而非将实验设施搬运到平台进行。考虑到目标、精度、易于操作以及读取,需要采用多种传感器。将数据转换为知识并确保数据(适当的大数据)以此种方式存储:灵敏且当前可调用,可用于未来分析。此文中基于以前十年的学习、IPPN当前实践与讨论,推荐了新一代表型组学,鼓励植物科学家、物理学家以及工程专家深度思考以及合作。

北京博普特科技有限公司提供了植物表型组学最全面的设备、传感器以及解决方案。欢迎广大客户到2019 IPPS国际表型会议展位拜访我们。

Review: New sensors and data-driven approaches—A path to next generation phenomics

https://doi.org/10.1016/j.plantsci.2019.01.011Get rights and content

Under a Creative Commons license

open access

Highlights

Strategies for future high throughput, non-destructive and cost-efficient measurement of plant traits are highlighted.

Use of low-cost and DIY approaches in phenomics provides opportunities for rapid prototyping and sensor development.

Robust protocols, data harmonization and provenance are critical to allow data reuse and cross validation of phenotypes.

Below-ground phenotyping is a major bottleneck and new technologies allowing the measurement of root-related traits are needed.

Abstract

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for “next generation phenomics” based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

 

Target Trait

Scale

Current limitations

Current method

Technologies under development (TRL)*

1. Growth, morphology

Heading and maturity

Plant

Resolution; accurate feature detection

Visual scoring

Cereal spike counts from images (7)

Winter hardiness, plant establishment

Plant/plot

Image pre-processing and automated analysis

Visual counting

Plant counts from images (7)

Biomass

Plant, canopy

Estimation of bio-volume vs actual weight

Fresh and oven dry weight

LIDAR (5)
SWIR (5)

Lodging

Plant

Subjective

Visual scoring

Video imaging to measure plant oscillation (5); ultrasonic distance sensors (5); force transducer (6)

Root development

Plant

slow, laborious manual methods

Soil coring; excavations; rhizotrons (controlled environment)

Ground penetrating radar (4);
Electromagnetic induction(4); Tomographic root imaging


2. Physiology

Water use efficiency

Plant, canopy

Measurement of water use and biomass slow, often only indirect estimations; scaling from tissue to crop

Destructive and gravimetric;
estimation via C and O isotopic ratios

LWIR, NIR (7); Thermal imaging (7); Fusion of chlorophyll fluorescence and thermal imaging (6)

Photosynthesis, transpiration

Leaf, plant, canopy

Upscaling, model specificity

Gas exchange; estimation via fluorescence at low O2, O isotopic ratio

Sun-induced chlorophyll fluorescence (6);
LIFT (6)

Leaf water status

Leaf

Slow, destructive, Low precision

gravimetric, psychrometry

Leaf clip SWIR (4); THz sensing

Nitrogen uptake efficiency

Plant

Indirect estimation of N

Isotopic tracer 15N tracers

Hyperspectral imaging for N concentration (6)

Shoot Nitrogen content

Plant

Indirect estimation of N (chlorophyll as surrogate), not accounting for grain N

Destructive and wet chemical analysis

Estimation via multi-spectral LiDAR (5); Hyperspectral imaging

Stem carbohydrates

Stem

Assays slow; cannot resolve fructan species; low precision via NIR

Colorimetric assays; HPLC, NIRS

Hyperspectal detection (5)

Grain protein content

Grain

Specificity; application of harvested grain, not proven on intact organs

NIRS,
wet chemistry

Hyperspectral sensing (6)

 

 


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