Wasswa Shafik

Yazd University, Dig Connectivity Research Laboratory (DCRLab), Research Lead

Followers

Following

Co-authors

Public Views

He is a member of IEEE, a computer scientist, an Information Technologist, and a Research Lab Head at the Dig Connectivity Research Laboratory (DCRLab), Kampala, Uganda. My research broadly integrates and focuses on developing principled computationally and statistically efficient models and algorithms for various machine learning problems in Smart Agriculture, Ecological Informatics, Computer Vision, Applied AI, Cybersecurity and Privacy, and Smart Cities. I attained a Bachelor in Information Technology at the Faculty of Science & Computing, Ndejje University, Kampala, Uganda; a Master in Information Technology Engineering (Computer and Communication Networks); and PhD in Computer Science at School of Digital Science, Universiti Brunei Darussalam, Brunei. He has received additional training from, among others, the National Institutes of Health, the US Department of Health and Human Services, and the Bloomberg School of Public Health, USA. Hundreds of scholarly publications, including those in prestigious peer-reviewed journal articles, numerous IEEE International, non-IEEE Conference proceedings, book chapters, and books have been published. Reviewer/editorial support of over twelve (Scopus, Compendex (Elsevier Engineering Index), and WoS International Journals, including Expert Systems With Applications, Scientific Reports and Computers and Electronics in Agriculture. I served in several capacities, including being departmental support for Mathematics for Data Science, Advanced Topics in Computing, and Advanced Algorithms. Prior to this, I served as a community data officer at Pace-Uganda, a research associate at TechnoServe, a research assistant at PSI-Uganda, a research lead at the Socio-economic Data Centre (SEDC-Uganda) and ag. managing director at Asmaah Charity Organisation.
Phone: +6738629801
Address: School of Digital Science, Universiti Brunei Darussalam Jalan Tungku Link, BE 1410, Brunei Darussalam

less

Interests

Uploads

Papers by Wasswa Shafik

A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

IEEE Access, 2023

Plant pests and diseases are a significant threat to almost all major types of plants and global ... more Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI), machine learning (ML), or deep learning (DL) methods and models to provide disease detection solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review (SLR) and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five major academic databases, namely Springer, IEEE Xplore, Scopus, Google Scholar, and ACM library. After deploying a comprehensive screening process, the review considered 176 final studies based on the importance of the method. Several crops, including grapes, rice, apples, cucumbers, maize, tomatoes, wheat, and potatoes, have tested mainly on the hyperspectral imagery and vision-centered approaches. Support Vector Machines (SVMs) and Logistic regression (LR) classifiers demonstrated an increased accuracy in experiments compared to traditional classifiers. Besides the image taxonomy, disease localization is depicted in these approaches as a bottle neck to disease detection. Cognitive CNNs with attention mechanisms and transfer learning are showing an increasing trend. There is no standard model performance assessment though the majority use accuracy, recall, precision, F1 Score, and confusion matrix. The available 11 datasets are laboratory and in-field based, and 9 are publicly available. Some laboratory-based datasets are considerably small, making them impractical in experiments. Finally, there is a need to avail models with fewer parameters, implementable on small devices and large datasets accommodating several crops and diseases to have robust models. INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.

Wasswa Shafik

Uploads

Papers by Wasswa Shafik

Log In