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ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 |
doi: 10.3389/fpls.2025.1511646
This article is part of the Research Topic Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume II View all 5 articles
Automatic Optimization of Region of Interest in Hyperspectral Images for Detection of Vegetative Indices in Soybeans
Provisionally accepted- Kyungpook National University, Daegu, Republic of Korea
Vegetative indices (VIs) are widely used in high-throughput phenotyping (HTP) for the assessment of plant growth conditions; however, a range of VIs among diverse soybeans is still an unexplored research area. For this reason, we investigated a range of four major VIs: normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), anthocyanin reflectance index (ARI), and chlorophyll reflectance index (CRI) in diverse soybean accessions. Furthermore, we ensured the correct positioning of the region of interest (ROI) on the soybean leaf and clarified the effect of choosing different ROI sizes. We also developed a Python algorithm for ROI selection and automatic VIs calculation. According to our results, each VI showed diverse ranges (NDVI: 0.60 ~ 0.84, PRI: -0.03 ~ 0.05, ARI: -0.84 ~ 0.85, CRI: 2.78 ~ 9.78) in two different growth stages. The size of pixels in ROI selection did not show any significant difference. In contrast, the shaded part and the petiole part had significant differences compared with the non-shaded and tip, side, and center of the leaf, respectively. In the case of the Python algorithm, algorithm-derived VIs showed a high correlation with the ENVI software-derived value: NDVI - 0.97, PRI - 0.96, ARI - 0.98, and CRI - 0.99. Moreover, the average error was detected to be less than 2.5% in all these VIs than in ENVI.
Keywords: Vegetative indices (VIs), hyperspectral imaging, ROI selection, python, ENVI
Received: 15 Oct 2024; Accepted: 07 Feb 2025.
Copyright: © 2025 Lee, Ghimire, Kim and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yoonha Kim, Kyungpook National University, Daegu, Republic of Korea
Jeong-Dong Lee, Kyungpook National University, Daegu, Republic of Korea
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