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.

Weeds might be defined as destructive plants that grow and compete with agricultural crops in order to achieve water and nutrients. Uniform spray of herbicides is nowadays a common cause in crops poisoning, environment pollution and high cost of herbicide consumption. Site-specific spraying is a possible solution for the problems that occur with uniform spray in fields. For this reason, a machine vision prototype is proposed in this study based on video processing and meta-heuristic classifiers for online identification and classification of Marfona potato plant (Solanum tuberosum) and 4299 samples from five weed plant varieties: Malva neglecta (mallow), Portulaca oleracea (purslane), Chenopodium album L (lamb's quarters), Secale cereale L (rye) and Xanthium strumarium (coklebur). In order to properly train the machine vision system, various videos taken from two Marfona potato fields within a surface of six hectares are used. After extraction of texture features based on the gray level co-occurrence matrix (GLCM), color features, spectral descriptors of texture, moment invariants and shape features, six effective discriminant features were selected: the standard deviation of saturation (S) component in HSV color space, difference of first and seventh moment invariants, mean value of hue component (H) in HSI color space, area to length ratio, average blue-difference chrominance (Cb) component in YCbCr color space and standard deviation of in-phase (I) component in YIQ color space. Classification results show a high accuracy of 98% correct classification rate (CCR) over the test set, being able to properly identify potato plant from previously mentioned five different weed varieties. Finally, the machine vision prototype was tested in field under real conditions and was able to properly detect, segment and classify weed from potato plant at a speed of up to 0.15 m/s.

Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.

Nondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for exploring the relationship between physicochemical and appearance characteristics of fruits at various ripening levels. In this regard, the purpose of the current study is to provide an intelligent algorithm for estimating two physical properties including firmness, and soluble solid content (SSC), three chemical properties viz. starch, acidity, and titratable acidity (TA), as well as detection of the ripening level of apples (cultivar Red Delicious) using video processing and artificial intelligence. To this end, videos of apples in orchards at four levels of ripeness were recorded and 444 color and texture features were extracted from these samples. Five physicochemical properties including firmness, SSC, starch, acidity, and TA were measured. Using the hybrid artificial neural network-difference evolution (ANN-DE), six most effective features (one texture and five color features) were selected to estimate the physicochemical properties of apples. The physicochemical estimation was then further optimized using a hybrid multilayer perceptron artificial neural network-cultural algorithm (ANN-CA). The results showed that the coefficient of determinations (R²) related to the prediction models for the physicochemical properties were in excess of 0.92. Additionally, the ripeness level of apples was estimated based on physicochemical properties using a hybrid multilayer perceptron artificial neural network-harmonic search algorithm (ANN-HS) classifier. The developed machine vision system examined ripeness levels of 1356 apples in natural orchard environments and achieved a correct classification rate (CCR) of 97.86%.