The main purpose of this research is to apply image processing for plant identification in agriculture. This application field has so far received less attention rather than the other image processing applications domains. This is called the plant identification system. In the plant identification system, the conventional technique is dealt with looking at the leaves and fruits of the plants. However, it does not take into account as a cost effective approach because of its time consumption. The image processing technique can lead to identify the specimens more quickly and classify them through a visual machine method. This paper proposes a methodology for identifying the plant leaf images through several items including GIST and Local Binary Pattern (LBP) features, three kinds of geometric features, as well as color moments, vein features, and texture features based on lacunarity. After completion of the processing phase, the features are normalized, and then Pbest-guide binary particle swarm optimization (PBPSO) is developed as a novel method for reduction of the features. In the next phase, these features are employed for classification of the plant species. Different machine learning classifiers are evaluated including k-nearest neighbor, decision tree, naï ve Bayes, and multi-SVM. We tested our proposed technique on Flavia and Folio leaf datasets. The final results demonstrated that the decision tree has the best performance. The results of the experiments reveal that the proposed algorithm shows the accuracy of 98.58% and 90.02% for the "Flavia" and "Folio" datasets, respectively.
This Letter presents a balanced-to-single-ended (BS) crossover for dual-band applications by using two rings included of composite right-and left-handed (CRLH) transmission lines (TLs) with controllable frequency ratio feature. Two dumbbell defected ground structures (DGSs) are employed through balanced TLs to suppress out-of-band common-mode. Theoretical analysis expresses that the frequency ratio can be controlled by suitable selecting of the element values and the number of designing cells of the CRLH TLs. Furthermore, desired impedances and electrical lengths of DGSs are obtained at the frequency of common-mode rejection by using even-and oddmode analyses. To validate the proposed structure, a dual-band BS crossover operating at 1.8 and 4.2 GHz is fabricated. Measurement results show that the fabricated proposed structure has good performance at the designed frequency bands.
Power systems include multiple units linked together to produce constantly moving electric power flux. Stability is very important in power systems, so controller systems should be implemented in power plants to ensure power system stability either in normal conditions or after the events of unwanted inputs and disorder. Frequency and active power control are more important regarding stability. Our effort focused on designing and implementing robust PID and PI controllers based on genetic algorithm by changing the reference of generating units for faster damping of frequency oscillations. Implementation results are examined on two-area power system in the ideally state and in the case of parameter deviation. According to the results, the proposed controllers are resistant to deviation of power system parameters and governor uncertainties.
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