Hager Sobeah

Online shopping started to grow all over the world in the last decade, predominantly in the last year due to COVID19. As a result of the lockdown and because many people did not want to take the risk of visiting stores not to be infected, customers direct their full attention to online shopping. That affected both customers and stores’ owners since customers always spend a long time trying to pick the right size and fail most of the time causing high percentage of returns which affects stores’ sales. In this paper part of the solution is introduced by collecting 5 anthropometric parameters from the user and the rest is predicted using an imputation strategy. Feature selection techniques are also applied and a 3D model for the human body is presented to the user on a mobile application.

Red Palm Weevil (RPW), is a lethal pest affecting different species of palm trees especially date palms. It is also reported to be a worldwide issue as a result of attacking more than thirty-five countries. Palms are never in safe hands once affected by RPW, unless it is detected in the early stages of infection. Although many endeavors and studies have been made to deal with this pest, none of them were successful enough to discuss a method that detects RPW in its early stages. This survey provides an overview of the detection methods used. Furthermore, it introduces a new hybrid technique for RPW detection in its early stages. The technique is based on combining thermal and hyperspectral imaging (HSI) as well as applying different image processing and deep learning techniques to get the optimum results in this field so far after applying enhancements to these methods.

Today’s palm trees diseases which cause a huge loss in production are extremely hard to detect either because these diseases are hidden inside the texture of the palm itself and cannot be seen by naked eyes or because it appears on its leaves which are hardly examined due to how far they really are from the ground. In this paper we’re interested in detecting three of the most common diseases threatening palms today, Leaf Spots, Blight Spots and Red Palm Weevil. Diagnosis of these diseases are done by capturing normal and thermal images of palm trees then, image processing techniques were applied to the acquired images. Two classifiers were used, CNN to differentiate between Leaf Spots and Blight Spots diseases and SVM for Red Palm Weevil pest. The results for CNN and SVM algorithms showed a success rate of accuracy ratio 97.9% and 92.8% respectively, these results are considered to be the best results in this domain as far as we know. The paper also includes the first gathered therm...

Wild Oat degrades the quality and yield of wheat plants. Machine learning technique is used to detect wheat wild oat disease. Digital camera records the image of a wheat differentiated between healthy wheat and wild oats. The captured image is processed to determine whether each test picture is diseased or not. The comparison result illustrates a diseased and uninfected plant taken from the test data. Our target is to differentiate between wild oats and wheat. Our motivation to do this project is that to our knowledge, no one tried to solve this problem until now; a lot of papers, experiments, and reports had been made to compare Wheat and any other crop not solving the existence of Wild Oats with the Wheat.