Muhammad Asim Saleem

Muhammad Ahtsam Naeem, Muhammad Asim Saleem, Muhammad Imran Sharif et al.

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features.

M. Saleem, Hugo Valle, Stephen Brown et al.

In the era of Big Data, with the increasing use of large-scale data-driven applications, the visualization of very large high-resolution images and extracting useful information (searching for specific targets or rare signal events) from these images can pose challenges to the current video-wall display technologies. At Bellarmine University, we have set up an Advanced Visualization and Computational Lab (AVCL) using a state-of-the-art next generation video-wall technology, called Hiperwall (Highly Interactive Parallelized Display Wall). The 16 feet wide by 4.5 feet high Hiperwall visualization system consists of eight display tiles that are arranged in a 4x2 tile format and has an effective resolution of 16.5 Megapixels. Using Hiperwall, we can perform interactive visual data analytics of large images by conducting comparative views of multiple large images in Astronomy and multiple data events in experimental High Energy Physics (HEP). Users can display a single large image across all the display tiles, or view many different images simultaneously on multiple display tiles. Hiperwall enables simultaneous visualization of multiple high resolution images and its contents on the entire display wall without loss of clarity. Hiperwall's middleware also allows researchers in geographically diverse locations to collaborate on large scientific experiments. In this paper we will provide a description of a new generation of display wall setup at Bellarmine University that is based on the Hiperwall technology, which is a robust visualization system for Big Data research.