The unpredictable nature of rip currents makes them a leading cause of coastal drowning incidents globally. Traditional methods fall short, necessitating an advanced surveillance system that can prioritize critical threats, enable autonomous decision-making with adaptive network control, and optimize resource allocation for enhanced coastal safety. Intent-based networking (IBN) plays a pivotal role in converting high-level intents into automated processes, enabling dynamic control and intelligent resource allocation in critical applications such as the Internet of Things (IoT) and unmanned aerial vehicle (UAV)-based coastal surveillance. This study proposes an artificial intelligence (AI)-powered, IBN-driven framework for coastal surveillance that leverages UAVs and IoT devices to enable real-time rip current analysis through advanced segmentation techniques. In our framework, UAVs with AI-powered IoT systems perform initial rip current analysis using lightweight deep-learning models. High-risk detections are prioritized through the closed-loop feedback mechanism of the IBN and transmitted to control rooms for validation and response, ensuring efficient resource utilization and adaptive surveillance. We expanded the rip current dataset to enhance the segmentation accuracy by incorporating additional samples from open-source platforms and applying diverse environmental conditions. We trained YOLO models and Mask R-CNN, which are suitable for real-time rip current analysis. In addition, we introduced a modified YOLOv11n-seg model, replacing the C3K2 block with C2F and optimizing the channels to reduce the parameters while maintaining accuracy. The best-performing models were tested on edge devices to evaluate the time complexity and reliability.

Timely and accurate crop damage classification (CDC) is vital for informed decision-making in the industry of precision agriculture. Traditional manual methods are slow and unreliable, whereas recent deep learning models, although accurate, are often too computationally intensive for resource-constrained environments. In this study, we present LNetCDC, a lightweight attention-based convolutional neural network tailored for CDC. The architecture integrates an EchoBlock for efficient feature extraction, combined with residual pathways enhanced by“Channelwise Refine”and“Dual Gate Attention”modules to emphasize critical spatial and channelwise features. Also, dilated convolutions are incorporated into deeper layers to capture multiscale contextual patterns. We evaluated our LNetCDC on a benchmark crop damage dataset, where it outperformed existing state-of-the-art (SOTA) models in terms of both accuracy and efficiency. Notably, it achieves around 2.3% gain in accuracy with only 0.86 million parameters compared with 1.13 million in the prior SOTA model for CDC. These results demonstrate the effectiveness and suitability of LNetCDC for real-time deployment on industrial edge devices.

This study presents a novel hybrid quantum‐classical framework for rice disease diagnosis, leveraging variational quantum circuits (VQCs) to address the limitations of traditional and deep learning models in precision agriculture. The proposed Quantum Variational Rice Disease Network (QVRDN) integrates quantum feature encoding, variational quantum processing, and adaptive optimization to achieve superior classification accuracy, efficiency, and robustness. Using a curated dataset of 3000 annotated rice leaf images spanning major disease categories, the QVRDN framework applies dimensionality reduction and quantum angle encoding to transform the image features into quantum states, which are then processed by parameterized quantum circuits for disease classification. Experimental results demonstrate that QVRDN outperforms classical models, including SVM, random forest, CNN, and ResNet50‐achieving, the highest accuracy of 97.8%, faster inference times, and greater resilience to noise and limited data. The compact design of the framework enables edge deployment without GPU dependency, making it suitable for resource‐constrained agricultural environments. By demonstrating the feasibility and advantages of quantum machine learning in crop health monitoring, this study establishes a foundation for quantum‐enhanced, data‐efficient agricultural diagnostics and paves the way for future advances in intelligent, field‐ready quantum geoinformatics systems.

The humankind had faced several pandemic outbreaks, and coronavirus illness (COVID-19) caused by severe, acute respiratory syndrome coronavirus 2, is designated an emergency by the World Health Organization (WHO). Recognition of COVID-19 is a challenging task. The most commonly used methods are X-ray and CT scans images to inspect COVID-19 patients. It requires specialised medical professionals to report each patient’s health manually. It is found that COVID-19 shows considerable similarity to pneumonia lung disease. Thus, knowledge learned from a model to diagnose pneumonia can be translated to identify COVID-19. Transfer learning method offers a drastic performance when compared with results from conventional classification. In this study, Image pre-processing is done to alleviate intensity variations between medical images. These processed images undergo a feature extraction which is accomplished using Q-deformed entropy and deep learning extraction. The feature extraction techniques are employed to remove abnormal markers from images, noise impedance from tissues and lesions. The traits acquired are integrated to differentiate between COVID-19, pneumonia and healthy cases. The primary aim of this model is to produce an image processing tool for medical professionals. The model results to inspect how a healthy or COVID-19 individual outperforms conventional models. The maximum accuracy of the collected data set is 99.68%.