Comparing Convolutional Neural Networks and Deep Metric Learning Methods for Classification of Export Watermelon (Citrullus lanatus) Varieties
Author:
Affiliation:
1. Universidad Tecnológica de Panamá,Facultad de Ingeniería de Eléctrica,Panamá,Panamá
2. Universidad Tecnológica de Panamá,Facultad de Ingeniería de Sistemas Computacionales,Panamá,Panamá
Funder
Secretaría Nacional de Ciencia, Tecnología e Innovación de Panamá (SENACYT)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9831187/9831454/09831572.pdf?arnumber=9831572
Reference29 articles.
1. De-termination of citrullus lanatus “sweet-16” ripeness using android-based application;villa;2021 International Conference on Electrical Communication and Computer Engineering (ICECCE),0
2. Using visible / near-infrared reflectance spectroscopy and chemomet-rics for the rapid evaluation of two panamanian watermelon (citrullus lanatus) varieties;rangel;2021 IEEE 30th International Symposium on Industrial Electronics (ISIE),0
3. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review
4. Research advances in nondestructive determination of internal quality in watermelon/melon: A review
5. Prediction of Total Soluble Solid Content in Intact and Cut Melons and Watermelons Using near Infrared Spectroscopy
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