@article {869,
	title = {Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data},
	journal = {Journal of Agricultural Sciences},
	volume = {26},
	year = {2020},
	pages = {339{\textendash}348},
	abstract = {Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using non-destructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network - Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92{\textpm}0.040 and 0.89{\textpm}0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.},
	doi = {https://doi.org/10.15832/ankutbd.523574},
	url = {https://dergipark.org.tr/en/pub/ankutbd/issue/56429/523574},
	author = {Yousef Abbaspour-Gilandeh and Sabzi, Sajad and Azadshahraki, Farzad and Karimzadeh, Rouhollah and Ilbeygi, Elham and J I Arribas}
}
@conference {871,
	title = {Non-intrusive image processing Thompson orange grading methods},
	booktitle = {2017 56th FITCE Congress},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A key issue in fruit export is classification and sorting for marketing. In this work image processing techniques are used to grad Thompson orange fruit. For this purpose, fourteen parameters were extracted, comprising area, eccentricity, perimeter, length/area, blue value, green value, red value, width, contrast, texture, width/area, width/length, roughness, and length. Adaptive neuro fuzzy inference system (ANFIS), linear, and nonlinear regression methods were used. Based on results, mean square error (MSB), sum squared error (SSE) and coefficient of determination (R 2 ) were 3.47e-08, 3.47e-07, 0.988 (ANFIS), 51.33, 4927.59, 0.866 (linear reg.) and 64.85, 6092.5, 0.832 (non-linear reg.), respectively. ANFIS model was shown as the best fit model based on previously listed performance evaluation criteria.},
	doi = {https://doi.org/10.1109/FITCE.2017.8093004},
	url = {https://ieeexplore.ieee.org/abstract/document/8093004},
	author = {Sabzi, Sajad and Yousef Abbaspour-Gilandeh and J I Arribas}
}
@conference {872,
	title = {A new method based on computer vision for non-intrusive orange peel sorting},
	booktitle = {2017 56th FITCE Congress},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As it is well-known, orange peel is used for making jam and oil. For this purpose, orange samples with high peel thickness are best. In order to predict peel thickness in orange fruit, we present a system based in image features, comprising: area, eccentricity, perimeter, length/area, blue value, green value, red value, wide, contrast, texture, wide/area, wide/length, roughness, and length. A novel identification solution based on the hybrid of particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) is proposed. In addition, principal component analysis (PCA) has been applied to reduce the number of dimensions, without much loss of information. Taguchi{\textquoteright}s robust optimization technique has been applied to determine the optimal setting for parameters of PSO, GA, and ANN. The optimal level of factors were: Number of Neuron in first layer=7, Number of Neuron in second layer=2, Maximum Iteration=400, Crossover probability=0.7, Mutation probability=0.1, and Swarm (Population) Size=200. Results for prediction of orange peel thickness based on levels that are achieved by Taguchi method were evaluated by five performance measures: the coefficient of determination (R 2 ), mean squared error (MSE), mean absolute error (MAE), sum square error (SSE), and root mean square error (RMSE), reaching values of 0.8571, 0.0123, 0.0924, 1.392, and 0.1109, respectively.},
	doi = {https://doi.org/10.1109/FITCE.2017.8093001},
	url = {https://ieeexplore.ieee.org/abstract/document/8093001},
	author = {Sabzi, Sajad and Yousef Abbaspour-Gilandeh and J I Arribas}
}
