Design and optimization of the energy system with the efficient method is one the major problem in recent years. The combined emergy-exergy-economic-environmental analysis is one of new methods selected for the optimization of energy systems. At present paper, first, optimal design of thermodynamic, exergo economic and exergo environmental was developed; the geothermal power plant was used as a complement to concentrated solar power (CSP) and then combined emergy-exergy-economic-environmental analysis was conducted. A standalone geothermal cycle (first mode), as well as hybrid Geothermal-Solar cycle (second mode) were investigated to generate the heating/cooling power of the building. The close similarity of the results of the exergy and emerge-economic analysis was very interesting. For standalone geothermal cycle, both exergo and emerge-economic analysis implied that highest value (6.02E-04 $/s and 3.1915E+09 sej/s) was related to turbine due to the heat generated by the impact of the blade, and the lowest value was related to ORC condenser. The exergo and emergo-economic analysis for geothermal-solar hybrid cycle, due to the increase in refrigerant pressure drop inside the coil, the evaporator (4.50E-03 $/s and 4.4699E+09 sej/s) and turbine (2.40E-03 $/s and 2.1920E+09 sej/s) had the highest amount. Also for standalone cycle, exergo and emergo-environmental implied that ORC turbine had the highest value of 1.26E-06 pts/s and 9.7201E+09sej/s. For hybrid geothermal-solar cycle, the evaporator (3.77E-06 pts/s and 6.1814E+08sej/s) and turbine (3.27E-06 pts/s and 6.37E+08 sej/s) had the highest amount of exergo and emergo-environmental. Solar power plants have only an initial cost and because solar energy is freely available to the system, so its economical exergy degradation is very low and has the lowest environmental exergy degradation. According to the results of the exergo-economic analysis of the hybrid power plant, the highest investment cost is related to solar power plant. It also has the lowest cost of exergy degradation because the environmental impact of fuel flow of solar panel is zero. The highest emerge-environmental rate of 3.3250E+09 (sej/s) was belonged to the solar power plant, but its environmental destruction rate was minimal because it does not consume fuel.

Recent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550–900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7‐layer network (CNN‐7) and a deep 18‐layer network (CNN‐18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D‐CNN‐18, compared to 90% for 3D‐CNN‐7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting. Practical Application: Orange bruises can reduce the market value of food, which is why the food processing industry needs to carry out quality inspections. An effective way to perform this inspection is by using hyperspectral images that can be processed with 2D or 3D models, either with deep or shallow neural networks. The results of the comparison performed in this work can be useful for the development of more accurate and efficient bruise detection methods for fruit inspection.

Nondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool. Regression coefficients ($\text{R}$) in estimated acidity, tissue firmness, and starch content properties were $\text{R}=0.930\pm 0.014$, $\text{R}=0.851\pm 0.014$, and $\text{R}=0.974\pm 0.006$, respectively, using only the three most effective wavelengths from the acquired spectra.

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.