| Titre : | AgriTechly Part 1 : Solutions in Plant Health and Crop Management |
| Auteurs : | RAHMANI Mohamed Elhadi, Directeur de thèse ; RAHMANI AbdElKader Seif El Islem, Auteur ; AIT AHMED Abdenour, Auteur |
| Type de document : | texte imprimé |
| Editeur : | univ DR taher moulay saida, 2024 |
| Format : | 176 p. / ill;fig; / 29cm. |
| Langues: | Anglais |
| Catégories : | |
| Mots-clés: | AgriTechly |
| Résumé : |
Sustainable agriculture is at the heart of global challenges, balancing the need to feed a growing population with the need to protect natural
resources and the environment. In this context AI and ML is the game changer, offering unprecedented opportunities to transform agriculture, increase productivity and sustainability. This dissertation starts with an overview of the need for sustainable agriculture, putting it in the context of population growth, climate change and resource depletion. The research covers multiple aspects of AI in agriculture, including precision farming, crop monitoring, pest management and decision support systems. Through experimental studies, data analysis and machine learning model development the dissertation shows how AI can optimize agricultural processes, reduce resource consumption and increase crop yields. Methodologically the dissertation uses various AI and ML techniques including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and transfer learning. By using large datasets, sensor technology and remote sensing imagery the research demonstrates how AI models can extract insights from complex agricultural data. Looking forward the dissertation outlines several future research and development directions in AI in agriculture including multimodal data fusion, explainability techniques, transfer learning and domain adaptation, collaborative AI systems and sustainability driven optimization criteria |
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Documents numériques (1)
sct02152 Adobe Acrobat PDF |
