Speech recognition is a crucial subject in human computer interaction area. The ability of a machine to recognize words and phrases in spoken language is speech recognition and then convert them to a machine-readable format. Digit recognition is a part of the speech recognition system. In this paper, three spectral based features including Mel Frequency Cepstral Coefficient (MFCC), Linear predictive coding (LPC) and formant frequencies are proposed to classify ten Kurdish uttered digits (0-9). The features are extracted from entire speech signal, and feed a pairwise SVM classifier. Experiments including each individual feature and different forms of fusion are conducted and the results are shown. The fusion of the features significantly improves the result and shows that the different features carry complementary information. The proposed model is experimented on the dataset that have been collected in Kurdistan. Key words: Speech recognition, MFCC, LPC, Formant frequencies, uttered digits, SVM

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community.

In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms.

Classifying flowers is a difficult activity because of the wide variety of flower species that have similar form. In this paper, a deep learning model for extracting features and classifying different flower types or species developed by using a popular method called Convolutional Neural Network (CNN). The identification system has been evaluated on a new dataset that has been designed in this work that collected flowers from Kurdistan. The dataset contains 1300 images of different flowers, 1040 images (%80) of which used for training purpose and 260 (%20) images used for test purpose, categorized into 26 classes. In addition, Various number of layers, activation function and pooling schemes were implemented to improve the classification rates. The recognition rate of proposed CNN model is 94.61%.