Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis
This study addresses the challenge of effectively selecting relevant variables and providing interpretable insights in spectroscopic analysis (1100–2498 nm) of corn quality (moisture, fat, protein, and starch), incorporating variable selection techniques and explainable artificial intelligence (AI)....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-08-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524000637 |