In this paper, artificial intelligence techniques (AIT) such as artificial neural network, naive bayes algorithm, random forest algorithm, K-nearest neighborhood (KNN) and support vector machine (SVM) are implemented to design an automatic identifier for the plant leaves. For this purpose, data of 637 healthy leaves consisting of 32 different plant species are used. 22 visual features (VF) of each leaf are extracted by using image processing techniques. These 22 VF are considered in 4 groups including dimension (D#6), color (C#6), texture (T#5) and pattern (P#5). In order to investigate the effects of these groups on the classifying performance, 15 possible different combinations from the 4 groups are constituted. The models are then trained via the data of 510 leaves, and their accuracy are tested through the data of 127 leaves. From the results of the test, SVM model with the accuracy of 92.91% is found to be the most successful identifier for combination including all groups. The next best result is achieved with the accuracy of 87.40% for the combination of D#6, C#6 and P#5 groups. Since the most important issue in the classification process is the use of the minimum number of VF, 16 most effective VF on the identification are determined by means of correlation-based feature selection (CFS) method. The best result for these 16 VF is also achieved with the accuracy of 94.49% by the SVM model. Then the performance of the proposed method is tested to identify the diseased and defected leaves. Therefore, 637 healthy and 33 diseased/defected leaves are put together. Randomly selected 536 leaves corresponding to 80% of all leaves are used for training and the remaining 134 leaves are used for testing, and identified with the accuracy of 92.53% by the SVM model. With this study, it is numerically revealed that the P#5 is the most effective feature group. Moreover, it has been determined that the most effective feature in the P#5 group is the feature of edge Fourier transform. The results point out that, if An' models are properly modelled and trained, they can be successfully and effectively applied to the identification of the plants even if there are diseased and defected samples.