Modern imaging techniques in plant nutrition analysis: A review

Daoliang Li, Cheng Li, Yuan Yao, Mengdi Li, Licheng Liu

Research output: Contribution to journalReview articlepeer-review

34 Scopus citations

Abstract

Plants show morphology, physiology and biochemical change when they encounter abiotic stress. Image-based plant nutrition analysis is based on this principle and focus on the relationships between plant nutrition content and phenotyping changes, such as leaf color, leaf texture, height, light reflectance ratio, etc. As a non-destructive method, imaging technology can be deployed to support automated plant diagnose and production. Current imaging technologies used in plant nutrition analysis include red–green–blue (RGB) imaging, imaging spectroscopy and fluorescence imaging. This article summarizes the development of imaging technologies for plant nutrition analysis over the past decade and presents their basic concepts and principles. The pros and cons of each imaging method are discussed and future research directions are highlighted. We focus on imaging applications describing the phenotyping of plant canopy instead of root architecture. Advancement in imaging technology has greatly accelerated the application of image-based plant nutrition analysis. This paper provides new ideas for researchers who are committed to plant nutrition analysis and production management.

Original languageEnglish (US)
Article number105459
JournalComputers and Electronics in Agriculture
Volume174
DOIs
StatePublished - Jul 2020

Bibliographical note

Funding Information:
The authors thank American Journal Experts for providing English language editing of this paper. We are also appreciated that Prof. Guoping Lian from Surrey University has made great contribution in improving English writing of this manuscript. In the end, we are grateful for financial support from the EU FP7 Framework Plan (Grant no. 619137), and Beijing Municipal Science and Technology Commission Project (Z171100001517016).

Funding Information:
The authors thank American Journal Experts for providing English language editing of this paper. We are also appreciated that Prof. Guoping Lian from Surrey University has made great contribution in improving English writing of this manuscript. In the end, we are grateful for financial support from the EU FP7 Framework Plan (Grant no. 619137), and Beijing Municipal Science and Technology Commission Project ( Z171100001517016 ).

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • Data analysis
  • Fluorescence imaging
  • High-throughput phenotyping platform
  • Image processing
  • Imaging spectroscopy
  • Plant nutrition analysis
  • RGB imaging

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