Order Article Reprints

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

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

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