EMITTER International Journal of Engineering Technology

An Attention based Vision Transformer for the Detection of Insect Pests in Castor Crop

2026-08-18T05:48:53+00:00

Castor (Ricinus communis L.) is a significant crop valued for its non-edible oil, yet its economic importance is compromised by insect pests causing substantial yield losses of 35-40%. This paper explores the efficiency of utilization of vision transformers for efficient pest classification. We propose CASTIPestViT, a Vision Transformer-based model specifically designed for insect pest detection in castor crops. The model integrates transfer learning and fine-tuning mechanisms, leveraging a pre-trained Vision Transformer (ViT) initially trained on ImageNet1k, and is fine-tuned on a custom dataset of castor insect pests. CASTIPestViT uses the self-attention mechanism of ViTs to capture global and local features of insect pests. The performance of CASTIPestViT is compared with six different pre-trained CNN models. The results obtained by the proposed model achieve a validation accuracy of 97.60% in insect pest detection and outperforming other state-of-the-art models in terms of precision, accuracy, and f1-score. The model offers a robust solution in early-stage insect pest detection to reduce yield losses. The efficiency and accuracy of the model make it suitable in sustainable crop management and smart agriculture systems for yield optimization.

Accurate Automatic Recognition of Iraqi License Plates Using YOLOv11n and Enhanced OCR Techniques

2026-08-18T05:48:34+00:00

Automatic License Plate Recognition (ALPR) systems are widely utilized for traffic monitoring, law enforcement, and security applications. While previous ALPR techniques have demonstrated high accuracy on uniform license plate formats, recognizing license plates in many countries particularly in Arabic-speaking regions remains a significant challenge due to the complex structure and diverse designs of the plates. Moreover, visual similarities between certain English letters and Arabic numerals often lead to misinterpretations by Optical Character Recognition (OCR) systems. This study proposes an efficient ALPR framework tailored for the new Iraqi license plates, employing image processing and deep learning (DL) techniques. The system integrates the YOLOv11n object detection model for accurate license plate localization, and OCR for character recognition. To address the OCR misclassification issue, particularly those caused by Arabic-English character similarities, the OCR component is enhanced using Character Index Checking (CCI) and Regular Expression Patterns (REP), resulting in more stable and accurate recognition outputs. The proposed method was evaluated on a real dataset of Iraqi license plates under varying illumination, viewpoints, and background complexity. Experimental results demonstrate strong end-to-end ALPR performance, achieving 100% plate detection accuracy, 99.8% recognition recall, and 99.5% mAP@0.5, confirming the robustness and practical effectiveness of the proposed system.

Exploring YOLO-Based Deep Learning Approaches for Fish Detection in Intelligent Aquatic Monitoring Systems

2026-08-18T05:48:14+00:00

The Advancements in precision aquaculture demand robust visual monitoring systems capable of accurate, real-time fish detection in complex underwater environments characterized by turbidity, occlusion, and dynamic illumination. While YOLO (You Only Look Once) architectures have demonstrated high efficiency in object detection tasks, their comparative performance for underwater fish detection remains underexplored, particularly across recent variants such as YOLOv5, YOLOv8, and YOLOv11. This study presents a systematic evaluation of three state-of-the-art YOLO models using a curated GlowFish dataset consisting of 533 annotated images across three fluorescent species. Data were acquired under controlled but visually diverse conditions using multi-angle imaging and standardized illumination. A uniform training pipeline, consistent annotation using the COCO format, and identical hyperparameters were applied across models to ensure fair benchmarking. Key evaluation metrics include precision, recall, mAP@0.5, and mAP@0.5:0.95. Experimental results reveal that YOLOv5 achieved the highest precision (0.963) and mAP@0.5 (0.967), while YOLOv8 delivered superior recall (0.930) and more balanced detection across species classes. YOLOv11 demonstrated architectural potential but showed greater sensitivity to class imbalance and reduced confidence stability. Visual analysis and confusion matrices further confirmed model-specific trade-offs in classification reliability and localization precision. This work contributes critical empirical insights into the selection of YOLO architectures for intelligent aquaculture systems, offering practical guidance for real-time aquatic monitoring deployments. Future research will extend this framework to multi-species, multi-environment datasets, integrate spatiotemporal behavioral tracking, and investigate deployment on resource-constrained edge-AI platforms, advancing the field toward interpretable and autonomous aquatic monitoring solutions.

Supporting Independent Prayer in Alzheimer’s Patients with an EEG-Enabled BCI System

2026-08-18T05:47:56+00:00

Alzheimer's disease (AD) significantly impairs cognitive functions, making independent activities, including Muslim prayers, challenging for patients. This study introduces an innovative Brain-Computer Interface (BCI) system leveraging Electroencephalography (EEG) signals to facilitate prayer practices for individuals with AD. Utilizing a 5-channel EEG headset to monitor attention levels, our system detects alpha and beta wave patterns to assess user focus. When attention diminishes, the system provides guidance to the next prayer step, ensuring continuity and support. Additionally, motion detection technology captures physical movements associated with prayer, enabling the classifier to learn and recognize different prayer postures from a dataset of two individuals. This approach aids AD patients in maintaining religious practices independently while significantly enhancing their psychological well-being by fostering autonomy and spiritual fulfillment. Our findings suggest that integrating EEG-based BCI systems with motion detection offers a promising avenue for supporting daily activities and improving quality of life for individuals with cognitive impairments.