| Titre : | Real-time crop health monitoring in precision agriculture using Geospatial data and deep learning | | Type de document : | document multimédia | | Auteurs : | Farouk Brachemi, Auteur ; Youcef Boudia, Auteur ; Mohamed El Habib Maicha, Directeur de thèse | | Editeur : | Laghouat : Université Amar Telidji - Département d'informatique | | Année de publication : | 2025 | | Autre Editeur : | Laghouat : Université Amar Telidji - Département d'informatique | | Importance : | 47 p. | | Accompagnement : | 1 disque optique numérique (CD-ROM) | | Note générale : | Option:Data science et artificial intelligence | | Langues : | Anglais | | Mots-clés : | Precision Agriculture NDVI UAV U-Net Real-time Inference Kafka Vegetation Indices Machine Learning Deep Learning | | Résumé : | Precision agriculture increasingly relies on advanced technologies to monitor crop health and optimize farming operations. This thesis presents a real-time crop monitoring system that leverages UAV-acquired multispectral imagery, deep learning, and a scalable data streaming infrastructure. High-resolution drone images are processed to generate vegetation indices such as NDVI, which serve as input for U-Net models. These models perform semantic segmentation and classification to detect various crop stress types, including nutrient deficiency, drydown, planter skips, water stress, and weed clusters. To ensure real-time processing and system scalability, the architecture integrates Apache Kafka for streaming UAV imagery and prediction outputs, supporting parallel operation across multiple producers and consumers. A React-based dashboard displays live predictions and metadata, enabling field operators to make timely and informed decisions. The system was validated using the Agriculture-Vision 2021 dataset and tested under simulated UAV deployment. The best segmentation performance was achieved using a U-Net model with RGB+NIR input at 512×512 resolution, reaching a Dice score of 0.70 and classification accuracy of 95%. In contrast, a NDVI-based model offered faster inference (3–5s) with slightly lower accuracy, making it suitable for resource-constrained environments. Kafka demonstrated low-latency image transmission (<2.5s) even with up to 15 parallel producers. | | note de thèses : | Mémoire de master en informatique |
Real-time crop health monitoring in precision agriculture using Geospatial data and deep learning [document multimédia] / Farouk Brachemi, Auteur ; Youcef Boudia, Auteur ; Mohamed El Habib Maicha, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique : Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 47 p. + 1 disque optique numérique (CD-ROM). Option:Data science et artificial intelligence Langues : Anglais | Mots-clés : | Precision Agriculture NDVI UAV U-Net Real-time Inference Kafka Vegetation Indices Machine Learning Deep Learning | | Résumé : | Precision agriculture increasingly relies on advanced technologies to monitor crop health and optimize farming operations. This thesis presents a real-time crop monitoring system that leverages UAV-acquired multispectral imagery, deep learning, and a scalable data streaming infrastructure. High-resolution drone images are processed to generate vegetation indices such as NDVI, which serve as input for U-Net models. These models perform semantic segmentation and classification to detect various crop stress types, including nutrient deficiency, drydown, planter skips, water stress, and weed clusters. To ensure real-time processing and system scalability, the architecture integrates Apache Kafka for streaming UAV imagery and prediction outputs, supporting parallel operation across multiple producers and consumers. A React-based dashboard displays live predictions and metadata, enabling field operators to make timely and informed decisions. The system was validated using the Agriculture-Vision 2021 dataset and tested under simulated UAV deployment. The best segmentation performance was achieved using a U-Net model with RGB+NIR input at 512×512 resolution, reaching a Dice score of 0.70 and classification accuracy of 95%. In contrast, a NDVI-based model offered faster inference (3–5s) with slightly lower accuracy, making it suitable for resource-constrained environments. Kafka demonstrated low-latency image transmission (<2.5s) even with up to 15 parallel producers. | | note de thèses : | Mémoire de master en informatique |
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Real-time crop health monitoring in precision agriculture using Geospatial data and deep learning
