996 0173 1699<\/a>
Code secret: 412444<\/p>\n\n\n\nMembres du jury :<\/strong><\/p>\n\n\n\nM. Mounim A. EL YACOUBI<\/strong>, Professeur, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these
M. Hassan BERBIA<\/strong>, Professeur, ENSIAS, MAROC – CoDirecteur de these
Mme Pilar MONTESINOS BARRIOS<\/strong>, Professeure, Universit\u00e9 de Cordoba, ESPAGNE – Examinateur
M. Huafeng QIN<\/strong>, Professeur, Chongqing Technology and Business University, CHINE – Examinateur
Mme Ghizlane KHAISSIDI <\/strong>, Professeure, Ecole Nationale des Sciences Appliqu\u00e9es de F\u00e8s (ENSAF) , MAROC – Examinateur
Mme Carole DELENNE<\/strong>, Professeure, universit\u00e9 d\u2019Aix Marseille, FRANCE – Rapporteur
M. Salah ERRAKI<\/strong>, Professeur, Universit\u00e9 Cadi Ayyad, MAROC – Rapporteur<\/p>\n\n\n\n\u00ab Application de mod\u00e8les avanc\u00e9s d’intelligence artificielle \u00e0 l’irrigation intelligente \u00e0 partir de donn\u00e9es capteurs et d’images satellitaires. \u00bb<\/h2>\n\n\n\npr\u00e9sent\u00e9 par Madame Hajar HAMMOUCH<\/h2>\n\n\n\n
R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\nDans cette th\u00e8se, nous abordons les d\u00e9fis urgents de la gestion de l’eau par l’analyse d’images agricoles et l’\u00e9tude de mod\u00e8les d’intelligence artificielle (IA) pour optimiser l’agriculture de pr\u00e9cision. Etant donn\u00e9 la menace croissante du changement climatique et la crise mondiale de l’eau, nous avons effectu\u00e9 une \u00e9tude syst\u00e9matique des technologies d’irrigation intelligente, avec une emphase particuli\u00e8re sur les capteurs IoT, la t\u00e9l\u00e9d\u00e9tection (RS: Remote Sesnsing) et les m\u00e9thodes d’IA. Cet examen exhaustif met non seulement en \u00e9vidence les approches existantes, mais ouvre \u00e9galement la voie \u00e0 de nouvelles solutions qui optimisent l’utilisation de l’eau et renforcent une agriculture durable. Pour r\u00e9pondre au manque critique d’ensembles de donn\u00e9es agricoles, nous avons propos\u00e9 et mis en \u0153uvre des r\u00e9seaux neuronaux convolutifs (CNN) et des r\u00e9seaux adversaires g\u00e9n\u00e9ratifs (GAN) pour pr\u00e9dire l’humidit\u00e9 du sol \u00e0 partir d’images a\u00e9riennes captur\u00e9es par des drones. En proposant un nouveau mod\u00e8le de GAN qui g\u00e9n\u00e8re conjointement des images synth\u00e9tiques et leurs vecteurs continus de v\u00e9rit\u00e9 terrain, nous avons am\u00e9lior\u00e9 de mani\u00e8re significative les performances des CNN dans des environnements o\u00f9 les donn\u00e9es sont limit\u00e9es. Les erreurs de pr\u00e9diction ont \u00e9t\u00e9 consid\u00e9rablement r\u00e9duites, ce qui prouve la puissance de l’augmentation des donn\u00e9es par GAN dans les t\u00e2ches de r\u00e9gression, un contexte d’augmentation des donn\u00e9es qui n’est pas pris en couvert par les GAN conventionnels. En outre, nous avons propos\u00e9 des mod\u00e8les d’IA hybrides, combinant des mod\u00e8les d’apprentissage profond et des mod\u00e8les d’apprentissage automatique exploitant des caract\u00e9ristiques fond\u00e9es sur l’expertise humaine, pour pr\u00e9dire la teneur en azote des cultures de sorgho – un facteur essentiel pour la sant\u00e9 des cultures – \u00e0 l’aide d’images RGB captur\u00e9es par des drones. Nous avons men\u00e9 cette recherche dans le cadre d’une collaboration avec l’Universit\u00e9 tch\u00e8que des sciences de la vie \u00e0 Prague (CZU) et l’Institut international de recherche sur les cultures des zones tropicales semi-arides (ICRISAT) \u00e0 Telangana, en Inde. En int\u00e9grant des indices spectraux aux architectures CNN, nous avons am\u00e9lior\u00e9 la pr\u00e9cision des pr\u00e9dictions d’azote, soutenant ainsi des pratiques agricoles plus pr\u00e9cises et durables. Gr\u00e2ce \u00e0 la fusion des technologies de l’IoT, de l’IA et RS, notre travail fournit des solutions innovantes pour relever les d\u00e9fis critiques de la gestion des ressources d’eau et de la durabilit\u00e9 environnementale.<\/p>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\nIn this thesis, we tackle the urgent challenges in water management, agricultural image analysis, by the investigation of artificial intelligence (AI) models to optimize precision farming. Recognizing the growing threat of climate change and the global water crisis, we conducted a systematic review of smart irrigation technologies, focusing on IoT sensors, remote sensing, and AI methods. This comprehensive review not only highlights existing approaches but also sets the stage for new solutions that optimize water use and enhance agricultural sustainability. To address the critical lack of agricultural datasets, we proposed and implemented convolutional neural networks (CNNs) and generative adversarial networks (GANs) to predict soil moisture from UAV-captured aerial images. By proposing a novel GAN model that generates conjointly synthetic images and their continuous ground truth vectors, we significantly enhanced CNN performance in data-scarce environments. This significantly reduced prediction errors, proving the power of GAN-driven data augmentation in regression tasks, a data augmentation setting not handled by conventional GANs. Additionally, we have proposed Hybrid AI models, combining deep learning models with Machine learning models leveraging human expert-based features, for predicting nitrogen content in sorghum crops\u2014an essential factor for crop health\u2014using UAV-captured RGB imagery. We have carried out this research in the context of collaboration with the Czech University of Life Sciences in Prague (CZU) and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Telangana, India. By integrating spectral indices with CNN architectures, we enhanced the accuracy of nitrogen predictions, supporting more precise and sustainable agricultural practices. Through the fusion of IoT, AI, and RS technologies, our work provides innovative solutions to address critical challenges in water resource management and environmental sustainability.<\/p>\n","protected":false},"excerpt":{"rendered":"
L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris et le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, Mod\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Madame Hajar HAMMOUCH Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : 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