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Oral

Emerging & Innovative Technologies (Interdisciplinary)

Hyperspectral Imaging for Salinity Stress Detection in Canola: Advancing Precision Agriculture with Novel Vegetation Indices

Tuesday, May 20, 2025
10:00 AM – 10:15 AM Alaska Daylight Time (AKDT)
Room: Room 6
Abstract Submission: Salinity stress poses a significant agricultural challenge, especially in areas with water scarcity where saline irrigation worsens soil salinization and reduces crop productivity. Canola (Brassica napus L.), the second-largest global oilseed crop, is commonly cultivated in these regions, making its salinity tolerance essential for sustainable farming. As climate change heightens water scarcity and salinity levels, understanding crop responses to these stresses is vital for food security. Recent advancements in hyperspectral imaging and machine learning have allowed rapid, non-invasive assessments of crop tolerance to salinity. This study used hyperspectral data from canola genotypes under controlled salinity treatments to explore their spectral responses and develop a novel vegetation index for salinity detection. By comparing hyperspectral bands to Sentinel-2 satellite bands, we refined salinity classification models, introducing new indices focused on near-infrared (NIR) and red-edge regions, which are critical for monitoring crop stress. One iteration with 300 hyperspectral bands, 30 vegetation indices, and a newly derived index improved accuracy by 4%. A subsequent iteration using 14 filtered bands and a second new index achieved an 82.61% accuracy, underscoring the importance of specific NIR wavelengths for detecting salinity stress. Several Sentinel-2 bands, including B1, B5, B6, B7, and B9, were less effective, highlighting the need for crop- and stress-specific spectral optimization. This research demonstrates the potential of hyperspectral imaging to improve salinity monitoring and contribute to precision agriculture by enhancing crop resilience in saline environments.

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