Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy.

Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and presence of mixed pixels are the main challenges in a multi-spectral image classification process. Most of the classical machine learning algorithms suffer from scoring optimal classification performance over multi-spectral image data. In this study, we propose stack-based ensemble-based learning approach to optimize image classification performance. In addition, we integrate the proposed ensemble learning with XGBoost method to further improve its classification accuracy. To conduct the experiment, the Landsat image data has been acquired from Bishoftu town located in the Oromia region of Ethiopia. The current study’s main objective was to assess the performance of land cover and land use analysis using multi-spectral image data. Results from our experiment indicate that, the proposed ensemble learning method outperforms any strong base classifiers with 99.96% classification performance accuracy.

This study aims to explore librarians’ perspective about the adoption of Linked Data technologies along with their level of interest to adopt these technologies in Pakistani university libraries. It also identifies their perceived hindrances that obstruct them to adopt and effectively implement Linked Data technologies in libraries. This is a quantitative study based on survey research design. To meet the objective of the study, a self-constructed and validated questionnaire was used to collect data. Findings show that university librarians believe in the effective adoption of Linked Data technology in libraries. They perceive that Linked Data technology can enhance navigation between the traditional online tools to access library resources and Linked Data will soon be the standard for creating metadata and records for information resources management in libraries. They are eager to attend events about Linked Data application in their libraries and willing to explore ways to incorporate Linked Data standards in bibliographic records management as well. Barriers do exist such as the general lack of awareness of basic Linked Data concepts and best practices for this emerging technology. To foster the research and to set Linked Data best practices in libraries and other cultural heritage institutions, there is a potential to invest in this area in terms of financial and social capital.