Remote sensing algorithms and their applications in plant phenotyping

ORCID

Bheemanahalli: https://orcid.org/0000-0002-9325-4901

MSU Affiliation

College of Agriculture and Life Sciences; Department of Plant and Soil Sciences; Department of Agricultural and Biological Engineering; Geosystems Research Institute

Creation Date

2025-11-19

Abstract

Assessing phenotypic traits associated with physiology, biochemistry, and plant health based on leaf spectral reflectance properties has become an important high throughput tool in agriculture research. Precise quantification and monitoring of plant responses to stresses (abiotic or biotic) help researchers' phenotype different genetic resources, map genetic loci, and choose donors for trait development. Studies have shown the potential use of leaf hyperspectral reflectance in assessing the plant phenotype under biotic and abiotic stress conditions. We compiled wavebands or reflectance strongly related to the lab and field-based measurements for pigments, leaf nitrogen, and leaf water content. This chapter also highlights the recent applications of hyperspectral reflectance in plant phenotyping, stress diagnosis, species classification, and robust statistical methods. Furthermore, we discuss the need for advanced analytical tools and their potential applications in plant phenotyping.

Publication Date

4-19-2023

Publication Title

Translating Physiological Tools to Augment Crop Breeding

First Page

337

Last Page

353

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Digital Object Identifier (DOI)

https://doi.org/10.1007/978-981-19-7498-4_15