Work

Hyperspectral Fingerprinting for Plant Health

Year: 2025

Type: conference-paper

Abstract: This paper investigates the efficacy of hyperspectral imaging and various vegetation indices for assessing plant health via predicting yield. We compare Bandwise and Pixelwise application methods for index calculation using the DeepPotato and HyperLeaf datasets. Results show strong correlations between specific indices and yield metrics: GNDVI ($R^2$=0.837) and CIGreen ($R^2$=0.836) were highly correlated with tuber dry mass in DeepPotato, while CIRed ($R^2$=0.868) showed the strongest link to grain weight in HyperLeaf. These findings underscore HSI's potential to provide valuable, non-destructive insights into crop health, serving as crucial proxies for yield prediction in precision agriculture.

Source: Ulster University Research Portal (Ulster University)

Authors: Melaugh, Melissa; id_orcid 0000-0002-8821-3152, Kerr, Dermot; id_orcid 0000-0002-5077-0658, Coleman, Sonya

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: green

OpenAlex ID: W7127775142

FWCI:

Cites:

Cited by:

Related to: