The Internet of Things (IoT) extend the connectivity into billions of IoT devices around the world. Artificial Intelligence (AI) is the best solution to manage huge data flow and storage in IoT network. Nowadays, IoT is becoming more popular with the invention of high internet speed and many advanced sensors that can be integrated into a microcontroller. Internet of Things relies on Artificial Intelligence technology which gives the term of Artificial Intelligence of Things (AIoT). AIoT is transformational and mutually beneficial for both technologies, as AI adds values to IoT through materials and software. This fusion impacts the revolution of industry 4.0 such as maintenance, production chains, optimization and logistics applied in industries to achieve increased productivity, profitability, efficiency, safety, and security.In this paper we will discuss the merge of Artificial Intelligence and Internet of Things, the soft-hard of AIoT and the impact of AIoT on the industry 4.0 applications.

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT.

The article presents Mine 4.0-MineCareerDB, a publicly available dataset of high-resolution image captured by a DJI Phantom 4 RTK drone specifically designed for analyzing mining careers. The dataset comprises a collection of 373 images depicting various mining operations and activities. Each image is georeferenced and offers a detailed view of mining activities, including the use of various equipment, infrastructure, and overall mining environment. This dataset has the potential to be a valuable resource for computer vision applications in the mining industry such as developing algorithms for identifying mining equipment, training deep learning models for safety analysis and optimization, and research on automation in mining operations. By making Mine4.0-MineCareerDB publicly available, we aim to stimulate further advancements in computer vision research and its applications in the mining sector. The dataset is available at: https://data.mendeley.com/datasets/c5s76mj4bm/5

The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies.

To implement a successful strawberry (Fragaria × ananassa) farming, fungal diseases must be detected in a timely manner so that informed crop protection decisions can be made. While field scouting is an option, it is manual and labor intensive. Scouting is also inaccurate and reduces efficiency due to micro-climatic lighting and field clutter, among other factors. StrawberryDualNet is a framework that supports Integrated Pest Management and automates symptom surveillance. We present dual-path CNN–Transformer fusion design that integrates two branches: a dynamic multi-scale convolution and a lightweight transformer. The former is able to capture fine-grained morphological lesion textures, while the latter captures overall contextual patterns. The two representations are fused through a learnable gating mechanism to decrease visual uncertainty amongst differing symptoms. We used a stratified five-fold cross-validation to evaluate the framework among five economically significant pathogens. Our approach significantly outperformed other automated scouting baselines, achieving 95.1% accuracy and 95.3% precision, respectively, and it is successful for Anthracnose, Gray Mold, Powdery Mildew, Rhizopus Rot, and Black Spot. The model is also scaled down compared to others (0.04 M parameters; 0.72 MB, 13–20× smaller than MobileNetV2/ShuffleNetV2) and is thus able to be deployed on devices that are lacking computational resources. For edge feasibility, we assessed reduced-precision inference; 16-bit floating point quantization preserved baseline performance at 83 FPS, whereas 8-bit integer quantization caused notable accuracy degradation. Overall, the proposed local–global fusion design provides an accurate, interpretable, and scalable tool for real-time disease phenotyping in precision horticulture.

This paper proposes a robust sliding-mode control (SMC) strategy for a single-phase PWM AC–DC boost rectifier intended for wireless electric vehicle charging systems. The controller is designed to achieve two main objectives: accurate synchronization of the input current with the grid voltage for power-factor correction and stable DC-link voltage regulation under load variations and parameter uncertainties. To improve practical implementation, a continuous approximation of the discontinuous SMC term is introduced to reduce chattering while preserving robustness. The novelty of this work lies in the unified current–voltage SMC formulation, which combines input current shaping for power-factor correction and DC-link voltage regulation within the same control framework for a receiver-side rectifier used in wireless EV charging. The proposed approach is evaluated in MATLAB/Simulink and compared with a conventional PI controller and a hybrid PI + SMC current-loop scheme. Simulation results demonstrate fast voltage dynamics, with a settling time of about 8 ms, effective tracking under variable DC-link reference changes, and improved input power quality. The input current total harmonic distortion (THD) is reduced to 0.73%, while the power factor increases up to 0.999. Under the considered operating conditions, the conversion efficiency reaches 97%.