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.

Nowadays, rapid growth of the Internet and digital multimedia technologies make it possible to duplicate data without any loss of quality and at a very low cost. In this regard, manipulation of documents will easily be accomplished by applying digital art and without the copyright owner's permission. To deal with situation, more diverse security requirements are introduced every day. Watermarking is considered as one of the methods used for achieving this purpose. Watermarking is focused on inserting a subtle signal between the host media data somehow it does not change the original data, but they can be extracted if necessary. They are also used as a claim for ownership of the digital effect. Various methods have been presented for watermarking so far. These methods have been developed to overcome the weaknesses of previous methods. Empirical wavelet transform is taken into account as a new method for hiding and extracting military digital images with respect to alpha composition. As such, it is resistant to noise, low pass filter and compression. Analytical studies showed that this method is more efficient than other methods from quantitative and qualitative point of view.