Learning techniques used in computer vision for food quality evaluation: a review

By C. Du, Da‐Wen Sun. 2006

392 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/f3e1a37695e5c9a54b9b0b51df671a84ee53dab7

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. M. Kamruzzaman
3. Sergio Cubero
4. Paul Allen
5. José Blasco
6. Gamal ElMasry
7. Nuria Aleixos
8. P. Talens

In Biomedical Engineering

1. Da‐Wen Sun
2. M. Kamruzzaman
3. Paul Allen
4. Gamal ElMasry
5. José Blasco
6. Sergio Cubero
7. Nuria Aleixos
8. P. Talens

In Industrial and Manufacturing Engineering

1. Da‐Wen Sun
2. Sachin S. Kamble
3. Angappa Gunasekaran
4. Paul Allen
5. M. Kamruzzaman
6. Gamal ElMasry

Citations in the corpus

• Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “, 2016), food quality evaluation (Du and Sun, 2006), detection of biotic stress in crop (Behmann et al.”
• Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review (2012, methodology): “Different pre-processing techniques are often used to mathematically treat the extracted spectral data from hyperspectral images to correct undesired effects such as light scattering, path length variations, and random noise resulting from variable physical sample properties or instrumental effects.”
• Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment (2012, background): “These problems are commonly alleviated by using techniques that retain most of the original information in fewer bands, but conserve the greatest amount of variability and the most significant information contained in the hyperspectral image (Du and Sun 2006).”
• A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, background): “Previous studies and datasets are also referred to validate the ML predictions obtained from the current datasets for quality output, improving the decision-making performance of the organization (Du and Sun, 2006).”
• Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines (2019, background): “necessary retain the most original information in a few bands, while preserving the greatest amount of variability and the most significant information (Du and Sun, 2006).”