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
Importance :	47 p.
Accompagnement :	1 disque optique numérique (CD-ROM)
Note générale :	Option:Data science et artificial intelligence
Langues :	Anglais (eng)
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, 2025 . - 47 p. + 1 disque optique numérique (CD-ROM).
Option:Data science et artificial intelligence
Langues : Anglais (eng)

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