Non-Destructive Seed Viability Assessment via Multispectral Imaging and Stacking Ensemble Learning

Ye Rin Chu, Min Su Jo, Ga Eun Kim, Cho Hee Park, Dong Jun Lee, Sang Hoon Che and Chae Sun Na ()
Additional contact information
Ye Rin Chu: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea
Min Su Jo: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea
Ga Eun Kim: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea
Cho Hee Park: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea
Dong Jun Lee: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea
Sang Hoon Che: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea
Chae Sun Na: Forest Bioresources Department, Baekdudaegan National Arboretum, Bonghwa 36209, Republic of Korea

Agriculture, 2024, vol. 14, issue 10, 1-15

Abstract: The tetrazolium (TZ) test is a reliable but destructive method for identifying viable seeds. In this study, a non-destructive seed viability analysis method for Allium ulleungense was developed using multispectral imaging and stacking ensemble learning. Using the Videometerlab 4, multispectral imaging data were collected from 390 A. ulleungense seeds subjected to NaCl-accelerated aging treatments with three repetitions per treatment. Spectral values were obtained at 19 wavelengths (365–970 nm), and seed viability was determined using the TZ test. Next, 80% of spectral values were used to train Decision Tree, Random Forest, LightGBM, and XGBoost machine learning models, and 20% were used for testing. The models classified viable and non-viable seeds with an accuracy of 95–91% on the K-Fold value ( n = 5) and 85–81% on the test data. A stacking ensemble model was developed using a Decision Tree as the meta-model, achieving an AUC of 0.93 and a test accuracy of 90%. Feature importance and SHAP value assessments identified 570, 645, and 940 nm wavelengths as critical for seed viability classification. These results demonstrate that machine learning-based spectral data analysis can be effectively used for seed viability assessment, potentially replacing the TZ test with a non-destructive method.

Keywords: seed viability test; multispectral imaging; stacking ensemble learning; Allium (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/10/1679/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/10/1679/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:10:p:1679-:d:1486165

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().