IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and detect healthy ones, using an RGB camera as a crucial part of our real-time autonomous greenhouses' robot along with using two datasets, plant-village and cotton dataset, to investigate the best convolutional neural network architecture. The first dataset contained 10,190 images from the plant-village open datasets; it includes four crops with ten distinct classes of diseased and healthy leaves. Moreover, the cotton dataset contained 2,204 images for training and 106 images for testing; it has four classes of diseased and healthy plants and leaves. Different network architectures were tested in this paper for the best suitable lightweight architecture for our mobile robot. Results show that the best performance is 99.908% which achieved by the VGG16 network. The highest accuracy of VGG16 obtained in our research makes it the best tool for our autonomous plant disease detection robot.

Object detection of autonomous vehicles presents a big challenge for
researchers due to the requirements of accuracy and precision in real-time.
This work presents a deep learning approach based on a dual architecture
design of the network. A highly accurate multi-class network of convolutional
neural networks (CNN) is presented for input data classification. A Region-
Based Convolutional Neural Networks (Faster R-CNN) network with a modified
Feature Pyramid Networks (FPN) is used for better detection of tiny objects and
You Only Look Once (YOLOv3) network is used for general detection. Each
network independently detects the existence of an object. The decision maps are
then fused and compared to decide whether an object is present or not. Faster
R-CNN with FPN model reported a higher intersection over Union (IoU) and
mean average precision (mAP) than the YOLOv3. This approach is reliable
demonstrating an upgrade on the existing state-of-the-art methods of fully
connected networks.

Sentiment Analysis (SA) is a field of Natural Language Processing (NLP) whose goal is to extract the emotion, sentiment or more general opinion expressed in a human-written text. Opinions and emotions play a central role in human life. Therefore, there are many academic researches in this field for processing many languages like English However, there is scarce in its implementation with addressing Arabic Sentiment Analysis (ASA). It is a challenging field where Arabic language has a rich morphological structure and there are many other defies more than in other languages. For that, the proposed model tackles ASA by using a Deep Learning approach. In this work, one of word embedding methods, such as a first hidden layer for features extracting from the input dataset and Long Short-Term Memory (LSTM) as a deep neural network, has been used for training. The model combined with Softmax layer is applied to turn numeric outputs from LSTM layer into probabilities to classify the outputs to positive or negative. There are two datasets that are used for training the model separately with each one. The first one is ASTD dataset as a dialectal Arabic type about different tweets from internet, the results with this dataset is compared with another academic work that used the same one. The results from this work outperforms through accuracy about 14.95% and F-score about 15.14% more than what performed in the previous work. The second one is HTL dataset as a modern standard Arabic type about opinions of reviewers on different hotels from several countries. This dataset is bigger in size than the first one to show the size effect on the results of this model. So, the accuracy increased about 11% and F-score about 10.8% more than what performed with the first dataset.