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.
| Original language | English |
|---|---|
| Pages | 91-98 |
| Number of pages | 8 |
| Publication status | Published (in print/issue) - 3 Sept 2025 |
| Event | IMVIP 2025 - Ulster University, Derry~Londonderry, Northern Ireland, Londonderry, United Kingdom Duration: 1 Sept 2025 → 3 Sept 2025 https://imvipconference.github.io/ |
Conference
| Conference | IMVIP 2025 |
|---|---|
| Country/Territory | United Kingdom |
| City | Londonderry |
| Period | 1/09/25 → 3/09/25 |
| Internet address |
Funding
Ulster University, School of Computing, Engineering and Intelligent Systems
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure -
SDG 12 Responsible Consumption and Production
Keywords
- Hyperspectral Image
- vegetation index
- plant health
- pixel-based
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