Proximal hyperspectral sensing of abiotic stresses in plants

Alireza Sanaeifar, Ce Yang, Miguel de la Guardia, Wenkai Zhang, Xiaoli Li, Yong He

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations

Abstract

Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.

Original languageEnglish (US)
Article number160652
JournalScience of the Total Environment
Volume861
DOIs
StatePublished - Feb 25 2023

Bibliographical note

Funding Information:
The research was funded by the Key R&D Projects in Zhejiang Province (Project No: 2022C02044 ) and National Natural Science Foundation of China (Project No: 32171889 and 32071895 ). The authors wish to express their sincere gratitude to Dr. Hamed Tavakoli for his invaluable assistance and advice in structuring the manuscript.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Abiotic plant stressors
  • Hyperspectral sensing
  • Proximal sensing
  • Remote sensing

PubMed: MeSH publication types

  • Journal Article
  • Review

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