| Etablissement | Université 8 mai 1945 de Guelma | | Affiliation | Département d'Electrotechnique et automatique | | Auteur | DJEBLI, Mohammed Amdjed | | Directeur de thèse | Bencheriet Chemesse ennehar (Professeur) | | Filière | Automatique | | Diplôme | Doctorat LMD | | Titre | Smart Agriculture: Intelligent System for Cereal Diseases Detection and Diagnosis. | | Mots clés | Smart Agriculture, Cereal Diseases, Deep Learning, Deep Neural Network, Disease detection, Diagnosis detection. | | Résumé | Cereals are a staple food in many developing countries, particularly in the Maghreb, making disease detection crucial for food security. However, traditional methods often fail to provide timely and accurate diagnoses. This research explores an innovative approach using deep learning to enhance disease detection in cereal crops within smart agriculture.
By leveraging deep neural networks, the proposed system aims for high precision and reliability in identifying crop diseases. Accurate detection will enable early interventions, reducing losses and improving productivity. The system's effectiveness will be tested through real-world datasets and field trials to ensure practical applicability.
Additionally, this study investigates how such technology can be integrated into existing farming practices, offering farmers actionable insights to safeguard crop health. By advancing smart agriculture, this research contributes to more sustainable and resilient farming, addressing environmental and economic challenges.
This thesis aims to create a system that uses deep learning to detect diseases in cereal crops. The goal is to provide fast and accurate results so farmers can take action quickly and reduce crop losses. The system will be tested with real data to ensure it works well in different farming conditions. It will also explore how to integrate this technology into current farming methods to help farmers improve productivity. In the end, this research hopes to support smart farming by offering a simple and effective way to protect crops and ensure food security.
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