In recent years, a significant number of people in Morocco have been commuting daily to Casablanca, the country’s economic capital. This heavy traffic flow has led to congestion and accidents during certain times of the day as the city’s roads cannot handle the high volume of vehicles passing through. To address this issue, it is essential to expand the infrastructure based on accurate traffic-flow data. In collaboration with the municipality of Bouskoura, a neighboring city of Casablanca, we proposed installing a smart camera on the primary route connecting the two cities. This camera would enable us to gather accurate statistics on the number and types of vehicles crossing the road, which can be used to adapt and redesign the existing infrastructure. We implemented our system using the YOLOv7-tiny object detection model to detect and classify the various types of vehicles (such as trucks, cars, motorcycles, and buses) crossing the main road. Additionally, we used the Deep SORT tracking method to track each vehicle appearing on the camera and to provide the total number of each class for each lane, as well as the number of vehicles passing from one lane to another. Furthermore, we deployed our solution on an embedded system, specifically the Nvidia Jetson Nano. This allowed us to create a compact and efficient system that is capable of a real-time processing of camera images, making it suitable for deployment in various scenarios where limited resources are required. Deploying our solution on the Nvidia Jetson Nano showed promising results, and we believe that this approach could be applied in similar traffic-surveillance projects to provide accurate and reliable data for better decision-making.

Every day, countless individuals with hearing or speech disabilities struggle to communicate effectively, as their conditions limit conventional verbal interaction. For them, sign language becomes an essential and often sole tool for expressing thoughts and engaging with others. However, the general public’s limited understanding of sign language poses a major barrier, often resulting in social, educational, and professional exclusion. To bridge this communication gap, the present study proposes a smart wearable glove system designed to translate Arabic sign language (ArSL), especially Moroccan sign language (MSL), into a written alphabet in real time. The glove integrates five MPU6050 motion sensors, one on each finger, capable of capturing detailed motion data, including angular velocity and linear acceleration. These motion signals are processed using an Artificial Neural Network (ANN), implemented directly on a Raspberry Pi Pico through embedded machine learning techniques. A custom dataset comprising labeled gestures corresponding to the MSL alphabet was developed for training the model. Following the training phase, the neural network attained a gesture recognition accuracy of 98%, reflecting strong performance in terms of reliability and classification precision. We developed an affordable and portable glove system aimed at improving daily communication for individuals with hearing impairments in Morocco, contributing to greater inclusivity and improved accessibility.

This work introduces an efficient and noise-robust automatic speech recognition (ASR) framework for real-time unmanned aerial vehicle (UAV) control using Modern Standard Arabic (MSA). The proposed approach tackles two major obstacles: the limited availability of Arabic speech resources and the harsh acoustic conditions caused by UAV operation, where propeller, wind, and surrounding noises often impair recognition accuracy. To address these challenges, we enhance a convolutional neural network (CNN) with Squeeze-and-Excitation (SE) attention modules, allowing the model to highlight task-relevant speech cues while attenuating noise-related artifacts. Training was carried out on a purpose-built dataset of 8,800 MSA drone command utterances, with experiments conducted under both clean and noise-augmented conditions. Noise augmentation included three representative disturbances (propeller, wind, and wave) at signal-to-noise ratios (SNRs) of 0, 10, and 20 dB. The system was further validated across seven SNR levels ranging from –5 dB to 30 dB and tested with an unseen noise source originating from a Caterpillar C18 generator. Results show that the clean-trained baseline achieved 98.64% accuracy on the clean test set, while noise-augmented training slightly boosted accuracy to 98.78% and markedly improved robustness. Under 0 dB SNR with the unseen generator noise, the proposed method delivered a 50.55% absolute accuracy improvement over the baseline. With an inference latency of only 0.0256 seconds, the system ensures real-time responsiveness, achieving an effective compromise between recognition performance, computational cost, and resilience to noise, thereby demonstrating its potential for reliable UAV command and control in adverse acoustic conditions.

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints.

Convolutional neural networks (CNNs) excel at extracting spatial and channel-wise features, but reliance on final-layer activations often underutilizes intermediate representations and weakens hierarchical cues. We propose RyNet, a theoretically grounded, modular, and CNN-agnostic dual-attention framework that integrates dynamic channel refinement with multi-level feature fusion in a single backbone. RyNet aggregates low-, mid-, and high-level feature maps, applies channel squeezing with learnable channel-wise attention scores to promote selective activation, and fuses recalibrated features via a lightweight summation strategy. This yields stage-to-stage coupling with implicit spatial emphasis at negligible computational cost. RyNet achieves hierarchical complementarity without branch duplication or concatenation–projection stacks. Integrated into VGG16 and ResNet18, RyNet improves CIFAR-10 test accuracy from 89.90% to 90.84% (a relative improvement of 1.04%) and from 90.42% to 91.74% (a relative improvement of 1.50%), respectively, On the PlantVillage (tomato) dataset, a custom CNN-based model integrated with RyNet achieves 99.61% accuracy, representing a relative improvement of 1.54%. Under identical datasets and training conditions, RyNet consistently and statistically outperforms SE and CBAM, and beyond this, demonstrates superiority over recent CNN–Transformer hybrids and other state-of-the-art methods. Feature-space analysis reveals a 121.9% increase in inter-class separability, while visualizations and interpretability evaluations confirm the effectiveness of dynamic channel selection under multi-level fusion. These results provide strong theoretical, empirical, and statistical validation for the proposed design. Overall, RyNet emerges as a principled, lightweight, and easily integrable module for enhancing CNN representations in resource-constrained environments, with significant performance gains and improved model explainability.

Transfer learning enables pretrained deep networks to adapt to new tasks but typically preserves their original computational structure, leaving much of the inference cost unchanged. Structured pruning can reduce this complexity; however, introducing sparsity during fine-tuning creates a competing optimization problem in which transferred representations must adapt while the network simultaneously determines which structures can be removed. We propose GASP, an adaptation-preserving structured pruning framework that treats task adaptation and structural sparsity learning as a coordinated optimization process, allowing for the sparse structure to emerge progressively as pretrained representations adapt to the target task. Rather than imposing structural decisions independently of this adaptation, GASP first establishes target-task adaptation through a warm-up phase, before progressively introducing sparsity pressure through an equation-driven schedule. In parallel, temperature annealing gradually sharpens input-aware continuous gates toward increasingly differentiated structural responses. Together, these mechanisms regulate the onset and progression of sparsification while structural importance evolves during fine-tuning. The learned gating responses are subsequently aggregated over the validation data and converted into a physically compact architecture through global Otsu-based thresholding and structural reconstruction, avoiding predefined sparsity ratios and iterative prune–retrain cycles. The same optimization principle is applied to channels in CNNs and MLP units in Swin Transformers without additional trainable mask parameters. Experiments with VGG16, EfficientNet-B0, and Swin-Tiny on CIFAR-10, PlantVillage, and PlantDoc show that GASP achieves accuracy improvements of up to 2.22%, with the corresponding configuration reducing parameters and FLOPs by 91.37% and 88.83%, respectively. Raspberry Pi 4 deployment further demonstrates that the learned structural sparsity translates into practical inference efficiency. Overall, the results demonstrate the effectiveness of coordinating representation adaptation and structural sparsification when deriving compact task-adapted models.