Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes

By S. Mahesh, Annamalai Manickavasagan, Digvir S. Jayas, Jitendra Paliwal, N. White. 2008

167 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9b7ff572a05dd411a98e7e764c595b0ce7648495

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Da‐Wen Sun
2. Hongbin Pu
3. Lu Wang
4. Zhiwei Zhu
5. Dan Liu
6. M. Kamruzzaman
7. Digvir S. Jayas
8. Jitendra Paliwal

In Biophysics

1. Da‐Wen Sun
2. Hongbin Pu
3. Digvir S. Jayas
4. Jitendra Paliwal
5. N. White
6. S. Mahesh
7. Zhiwei Zhu
8. Lu Wang

In Molecular Biology

1. Digvir S. Jayas
2. Da‐Wen Sun
3. N. White
4. Jitendra Paliwal
5. S. Mahesh
6. Hongbin Pu

Citations in the corpus

• Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, methodology): “…et al. 2012), therefore, the technique has been widely studied to evaluate and classify quality of cereal grains including wheat (Toews et al. 2007; Mahesh et al. 2008), rice (Chang et al. 2013; Wang et al. 2014), oat (Serranti et al. 2013), barley (Milics et al. 2008), and sorghum (Manley et al.…”
• Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice (2015, methodology): “HSI has been widely used in the evaluation of cereal quality, including wheat grade identification (Mahesh et al. 2008) and maize kernel hardness classification (Williams et al. 2009).”
• Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat (2014, methodology): “Near-Infrared Hyperspectral Imaging System and Image Acquisition Procedures In this study, the NIR hyperspectral imaging system reported by Mahesh et al. (2008, 2011, 2014) and Singh et al. (2009) was used and is described below for enhanced readability of this manuscript.”
• Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review (2012, background): “…in apple, strawberry, and orange (Peng and Lu, 2008; Nagata et al., 2006; ElMasry et al., 2007; Liu et al., 2008), wheat kernels classification (Mahesh et al., 2008; Choudhary et al., 2009), meat quality assessment (Naganathan et al., 2008a; Qiao et al., 2007), poultry carcass inspection (Park…”
• Deep learning for smart fish farming: applications, opportunities and challenges (2020, background): “Fish are sensitive to environmental changes, and they exhibit a series of responses to changes environmental factors through behavioral changes (Saberioon et al., 2017 ; Mahesh et al., 2008).”