Edeh Michael Onyema

Agriculture 5.0 marks a shift in farming through technologies like digital twins, AI, robotics, and real-time 3D spatial modeling. A key innovation is the use of 3D point cloud methods for accurate virtual modeling of agricultural environments. This study compares eight 3D reconstruction tools—Agisoft, Pix4DMappers, ODM, DJi Terra, Meshroom, Regard3D, Reality Capture, and 3DF Zephyr—based on geometric integrity in five shape metrics: sphericity, planarity, linearity, anisotropy, and curvature. Using photogrammetry datasets, shape-index scoring, and regression analysis, we evaluated their suitability for digital twin use. Results show no single tool excels in all aspects; software choice should depend on specific goals. Agisoft scored high in curvature and sphericity, ODM performed best overall, and Meshroom and Regard3D suited high-anisotropy designs. The study highlights the importance of volumetric and curvature accuracy in biological modeling and promotes open-source tools for cost-effective digital twin deployment in precision farming.

Agriculture 5.0 marks a new era in food production driven by advanced technologies and human-centered artificial intelligence. Unlike Agriculture 4.0, which focuses mainly on efficiency and innovation, Agriculture 5.0 promotes collaboration where AI supports human decision-making. This study explores the ethical, social, and environmental impacts of human-centric AI in smart farming. Through case studies, statistical analysis, and comparative research, we show how open, inclusive, and sustainable AI can enhance agricultural productivity, efficiency, and farmer satisfaction. Task-based evaluations and ANOVA tests highlight the advantages of human-focused AI over traditional models. Beyond technical gains, the paper addresses ethical dilemmas, digital inequality, and data bias, proposing a cooperative model for agricultural tech. Embracing this human-first approach is vital for creating smart, fair, and sustainable agriculture.

5 A data-driven framework for future healthcare diagnosis through predictive analytics was published in Drug Discovery and Telemedicine on page 59.

The rapid development of digital technologies and artificial intelligence is transforming the intellectual environment in which higher education operates. When information becomes universally accessible and intelligent systems are capable of generating explanations and proposing solutions to complex problems, the traditional educational model based primarily on memorization and the reproduction of knowledge becomes increasingly insufficient. This paper examines the implications of this transformation for the purpose and structure of higher education from an interdisciplinary perspective. Drawing on insights from cognitive psychology, decision theory, philosophy of knowledge, and educational theory, the study argues that the central goal of education in the age of artificial intelligence should shift from the transmission of information toward the development of analytical thinking and conceptual understanding. Particular attention is given to the distinction between searching for existing answers and understanding the underlying structure of problems. The paper proposes a framework of meaning-centered education that emphasizes problem formulation, interpretation, and human–AI cognitive collaboration. The study also discusses implications for teaching methods, curriculum design, and systems of knowledge assessment, including the increasing relevance of open-resource examinations that evaluate analytical reasoning rather than memorization.

Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To address this, the study introduces a novel deep learning-based approach to enhance Mpox detection by integrating a hair removal process with an upgraded U-Net model. The research developed the “Mpox Skin Lesion Dataset (MSLD)” by combining images of skin lesions from Mpox, chickenpox, and measles. The proposed methodology includes a pre-processing step to effectively remove hair from dermoscopic images, improving the visibility of skin lesions. This is followed by applying an enhanced U-Net architecture, optimized for efficient feature extraction and segmentation, to detect and classify Mpox lesions accurately. Experimental evaluations indicate that the proposed approach significantly improves the accuracy of Mpox detection, surpassing the performance of existing models. The achieved accuracy, recall, and F1 scores for Mpox detection were 90%, 89%, and 86%, respectively. The proposed method offers a valuable tool for assisting physicians and healthcare practitioners in the early diagnosis of Mpox, contributing to improved clinical outcomes and better management of the disease.

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-of-the-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.