Pourdarbani, R.; Sabzi, S.; Hernández-Hernández, M.; Hernández-Hernández, J.L.; Gallardo-Bernal, I.; Herrera-Miranda, I.
Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm. Plants 2020, 9, 1547.
https://doi.org/10.3390/plants9111547
AMA Style
Pourdarbani R, Sabzi S, Hernández-Hernández M, Hernández-Hernández JL, Gallardo-Bernal I, Herrera-Miranda I.
Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm. Plants. 2020; 9(11):1547.
https://doi.org/10.3390/plants9111547
Chicago/Turabian Style
Pourdarbani, Razieh, Sajad Sabzi, Mario Hernández-Hernández, José Luis Hernández-Hernández, Iván Gallardo-Bernal, and Israel Herrera-Miranda.
2020. "Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm" Plants 9, no. 11: 1547.
https://doi.org/10.3390/plants9111547
APA Style
Pourdarbani, R., Sabzi, S., Hernández-Hernández, M., Hernández-Hernández, J. L., Gallardo-Bernal, I., & Herrera-Miranda, I.
(2020). Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm. Plants, 9(11), 1547.
https://doi.org/10.3390/plants9111547
