@article {863,
	title = {A three-variety automatic and non-intrusive computer vision system for the estimation of orange fruit pH value},
	journal = {Measurement},
	volume = {152},
	year = {2020},
	pages = {107298},
	abstract = {An automatic 3-variety computer vision orange fruit pH value assessment system in the visible-range is presented, including each 100 different color images from Bam, Blood and Thomson orange of which the true pH has been measured and is known in advance. A total of 452 features are extracted from segmented orange color images. Results with repeated trials include: true versus estimated mean pH values, true minus estimated pH values boxplots, fitness regression dispersion plots and various error measure boxplots, for both single orange variety as well as the three-orange varieties altogether (test set), showing consistent results over all three orange varieties. Regression coefficient for pH estimation in Bam, Blood and Thomson orange varieties, were 0.950, 0.935 and 0.957, respectively. Results show that the hybrid ANN-ABC estimates pH values in orange quite similarly among orange varieties, implying that properly pH values estimation is possible for different orange varieties, regardless of orange type.},
	doi = {https://doi.org/10.1016/j.measurement.2019.107298},
	url = {https://www.sciencedirect.com/science/article/pii/S0263224119311625},
	author = {Sabzi, Sajad and Javadikia, Hossein and J I Arribas}
}
