This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, $94\%$ using SVM and $63\%$ using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.

“You are imparting human qualities to it because it looks human—but I assure you: it is not. If it were a box on wheels, I would not be facing this opposition.” Commander Bruce Maddox spoke these words during a Star Trek, The Next Generation episode as Lieutenant Commander Data faced an enquiry into his sentience. Similarly, this literature review explores the role of generative artificial intelligence (GenAI) in education, specifically examining its function as a learning assistant within the Information and Communication Technology (ICT) discipline in higher education. Using AI‐powered tools such as Litmaps and Google Scholar, we conducted an iterative search and reviewed 26 peer‐reviewed articles, focusing on the use of GenAI tools as learning assistants in ICT education. We adhered to predefined inclusion and exclusion criteria to ensure methodological rigour, excluding non‐ICT populations, secondary sources, and non‐peer‐reviewed literature. The findings highlight the manifold advantages of integrating GenAI into ICT education, including its ability to diagnose programming code, provide tailored feedback, and serve as a valuable learning aid. However, significant challenges were also identified, particularly the tendency of novice programmers to uncritically accept GenAI‐generated output, concerns over academic integrity violations, and issues surrounding the privacy of personal information. We pinpoint that the integration of GenAI influences four key components: Andragogy, Assessment, Integrity, and Privacy. As GenAI becomes more entrenched in educational practices, this research provides a foundation for further exploration and refinement to maximise its transformative potential in higher education.

“The intelligence that was formed on the Enterprise didn’t just come out of the ship’s systems. It came from us. From our mission records , personal logs , holodeck programs , our fantasies.” This fictitious quote from Captain Jean-Luc Picard of the Starship Enterprise in the 1990s, though predating the advent of generative AI (GenAI) technologies, reflects a key truth: the efficacy of artificial intelligence is fundamentally tied to the quality of human input and interaction. GenAI tools are most valuable when they augment rather than replace human cognition. Guided by this principle, our study investigates the potential of GenAI tools as an adjunct or assistant to student learning in ICT education at universities. Using structured focus groups conducted across three institutions, we explored student perspectives on GenAI’s utility, challenges, learning outcomes and skills development. Participants generally expressed positive attitudes towards GenAI, recognising its time-saving and problem-solving capabilities, but also highlighted concerns about accuracy, ethical usage, and the necessity for guidance on effective utilisation. The findings of the thematic analysis informed the development of the GROW-AI framework, a holistic strategy for integrating GenAI tools into educational practices, addressing components that included guidelines, resources, oversight, workforce preparation, and awareness. This framework provides actionable insights for institutions seeking to harness the potential of GenAI while mitigating its risks, fostering a balanced approach to AI in education.