Integrating Shallow and Deep Features for Precision Evaluation of Corn Grain Quality: A Novel Fusion Approach

Integrating Shallow and Deep Features for Precision Evaluation of Corn Grain Quality: A Novel Fusion Approach

Abstract This study investigates the precision evaluation of corn grain quality, focusing on categorizing seeds into four classes: broken, discolored, pure, and silk cut. We evaluated 13 pre-trained CNN models, including AlexNet, VGG19, and ResNet, with AlexNet emerging as the top performer, achievi...

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Bibliographic Details
Main Authors: Kunal Mishra, Santi Kumari Behera, A. Geetha Devi, Prabira Kumar Sethy, Aziz Nanthaamornphong
Format: Article
Language:English
Published: Springer 2025-06-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Shallow features
Deep features
Image fusion
Support vector machine
Grain quality evaluation
Classification
Online Access:https://doi.org/10.1007/s44196-025-00889-2
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https://doi.org/10.1007/s44196-025-00889-2

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