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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 emergeeconomic analysis was very interesting. For standalone geothermal cycle, both exergo and emerge-economic analysis implied that highest value (6.02E-04 $/s and 3.1915Eþ09 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 4.4699Eþ09 sej/s) and turbine (2.40E-03 $/s and 2.1920Eþ09 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 9.7201Eþ09sej/s. For hybrid geothermal-solar cycle, the evaporator (3.77E-06 pts/s and 6.1814Eþ08sej/s) and turbine (3.27E-06 pts/s and 6.37Eþ08 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 3.3250Eþ09 (sej/s) was belonged to the solar power plant, but its environmental destruction rate was minimal because it does not consume fuel.

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Taking into account that traditional agricultural methods reduce the farm performance and make agriculture economically ineffective, development of intelligent machinery is essential for improving the quality of crops and agricultural activities. The most important issue in development of agricultural technologies lies in users’ willingness to adopt it. Therefore, the purpose of this study is to investigate the key factors of Davis’s model in automation acceptance in agriculture. Presented paper describes an applied research with a survey approach through a questionnaire. Questionnaire data were collected from 378 people and respondents include university students, farmers and experts in a randomized sampling from the Ministry of Agriculture. Firstly, the questionnaire data were described in a form of statistical numerical characteristics. Secondly, in order to verify the data normality, the Kolmogorov-Smirnov test was calculated in SPSS and the relationship between the variables was investigated in the conceptual model. Subsequently, hypotheses were tested via appropriate statistical models using LISREL and SPSS software. The results showed that for all hypotheses, the T-test exceeds 1.96 and the significance level is less than 0.05. In such a manner, all hypotheses were confirmed at 95% level, and the path coefficients in the hypotheses H1, H2 and H6 were negative – indicating the negative effect of the independent variable on the dependent variable in the hypothesis. In the other hypotheses, these were positive – indicating the positive effect of the independent variable on dependent variable. By means of modelling, it was found that there was an inverse relationship between social and individual factors with perceived usefulness, as well as an inverse relationship between social factors with perceived ease-of-use, while there was a positive and significant relationship between other factors. According to a set of fitting indices, the research conceptual model was appropriate and Cronbach’s coefficients for each factor were greater than 0.7, suggesting that the questionnaire was valid. On the basis of findings, the better a person understands the usefulness of automation, the more likely this person is to adopt it. Since the risk and issues related to it play an important role in farmers’ decision making, it is recommended that future studies address the issue of risk in adopting precision farming technology.

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.

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There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.

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.