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PLANT TISSUE ANALYSIS MARKET ANALYSIS

Plant Tissue Analysis Market, by Component (Platform (Cloud-based and On-premises), Services), by Terminal Type (Desktop Based and Mobile Based), by End User (Agricultural Companies, Research, Others) by Region (North America, Latin America, Europe, Asia Pacific, Middle East, and Africa)

  • Published In : May 2023
  • Code : CMI5765
  • Pages :189
  • Formats :
      Excel and PDF
  • Industry : Medical Devices

Market News

Global Plant Tissue Analysis Market: Key Developments

In August 2022, Element Materials Technology, a global materials testing, inspection, and certification company, acquired Singapore Test Services (STS), a leading testing, inspection, calibration, and certification provider. This acquisition strengthened Element Materials Technology’s position and growth plans in Southeast Asia.

In June 2022, Lifeasible, a biotechnology company, launched analysis services for plant stress tolerance. It provides a wide range of analysis services for the research of plant resistance including the determination of plant stress response indicators.

Global Plant Tissue Analysis Market: Restraint

Availability of Alternative Method for Plant Monitor

The availability of alternative methods to monitor crop nutrient status is expected to hamper the growth of the global plant tissue analysis market, over the forecast period. For instance, multispectral imaging (MSI) and hyperspectral imaging (HSI) have made the monitoring of field crop health to aid decision-making and the application of spatially and temporally variable agronomic inputs possible. Spectral imaging has played a vital role in facilitating crop monitoring to aid the decision-making in the implementation of spatially variable agronomic practices and/or inputs. Driven by cutting-edge data processing techniques, spectral imaging for field crop monitoring has become a prominent research area in the remote sensing community. Due to technical advances, the remote sensing community now has access to both dense time series data and high-spatiospectral-resolution images, without the need to approximate the compromised components using fusion methods. Moreover, machine-learning and deep-learning approaches have substantially enhanced the processing and analysis of spectral information.

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