Version 1
: Received: 28 April 2024 / Approved: 29 April 2024 / Online: 30 April 2024 (15:27:57 CEST)
How to cite:
Banerjee, S.; Reynolds, J.; Taggart, M.; Daniele, M. A.; Bozkurt, A.; Lobaton, E. Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis. Preprints2024, 2024041949. https://doi.org/10.20944/preprints202404.1949.v1
Banerjee, S.; Reynolds, J.; Taggart, M.; Daniele, M. A.; Bozkurt, A.; Lobaton, E. Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis. Preprints 2024, 2024041949. https://doi.org/10.20944/preprints202404.1949.v1
Banerjee, S.; Reynolds, J.; Taggart, M.; Daniele, M. A.; Bozkurt, A.; Lobaton, E. Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis. Preprints2024, 2024041949. https://doi.org/10.20944/preprints202404.1949.v1
APA Style
Banerjee, S., Reynolds, J., Taggart, M., Daniele, M. A., Bozkurt, A., & Lobaton, E. (2024). Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis. Preprints. https://doi.org/10.20944/preprints202404.1949.v1
Chicago/Turabian Style
Banerjee, S., Alper Bozkurt and Edgar Lobaton. 2024 "Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis" Preprints. https://doi.org/10.20944/preprints202404.1949.v1
Abstract
Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.
Keywords
Crop Health Monitoring; Drought Stress Detection; Remote Sensing; Near Infrared; Pixel Extraction; Classification
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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