Nipon Parinyavuttichai. The application of convolution neural network for coconut maturity classification. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2021.
The application of convolution neural network for coconut maturity classification
Abstract:
Coconut's sweetness consistency has a close connection with the quality of coconut by-products. And this is due to multiple factors, one of which is the maturity of coconuts. Maturity classification is, therefore, the main concern for both consumers and manufacturers. However, currently, the maturity classification process is mainly based on individual experience and skills developed throughout time in the industry. Our research proposes an automatic maturity classification with deep learning, which could minimize the maturity classification time cost. The fully trained CNN structures with fine-tuning of transfer learning from well-known models were analyzed and compared. The experimental results showed that our proposed CNN structures achieved the best validation accuracy by 4-fold cross-validation results with 97.27% with binarized saturation images, and 89.42% with HSV images.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2021
Modified:
2026-04-08
Issued:
2026-04-08
บทความ/Article
application/pdf
BibliograpyCitation :
In Chiang Mai University. Faculty of Engineering. 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2021) (pp.112-115). Chiang Mai : Chiang Mai University