Percorrer por autor "Masmoudi, Lhoussaine"
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- Embedding a real-time strawberry detection model into a pesticide-spraying mobile robot for greenhouse operationPublication . Amraoui, Khalid El; Ansari, Mohamed El; Lghoul, Mouataz; Alaoui, Mustapha El; Abanay, Abdelkrim; Jabri, Bouazza; Masmoudi, Lhoussaine; LUÍS VALENTE DE OLIVEIRA, JOSÉAbstract: The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 × 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices.
- A super resolution method based on generative adversarial networks with quantum feature enhancement: Application to aerial agricultural imagesPublication . El amraoui, Khalid; Pu, Ziqiang; Koutti, Lahcen; Masmoudi, Lhoussaine; Valente de Oliveira, JOSÉSuper-resolution aims to enhance the quality of a low-resolution image to create a high-resolution one. Remarkable advances are witnessed in this field using machine learning techniques. This paper presents a superresolution method based on generative adversarial networks (GAN) with quantum feature enhancement. The proposed framework uses a feature enhancement layer inspired by the quantum superposition principle. The layer was added to the state -of -the art super-resolution GAN (SRGAN) original model to enhance its performance. The model was trained and evaluated using two publicly available high-resolution aerial images datasets taken by an unmanned aerial vehicle. A set of statistically significant experiments are reported to show its performance. The structural similarity index metric (SSIM), t-distributed stochastic neighbor embedding (t -SNE) and peak signal-to-noise ratio (PSNR) are adopted to evaluate the performance of this proposal against SRGAN model. Results show that this proposal outperforms SRGAN in term of image reconstruction quality by 8% in similarity.