Computer vision for smart Farming and Agriculture practices
Computer vision for smart Farming and Agriculture practices
The integration of artificial intelligence (AI) in agriculture is revolutionizing traditional agricultural practices, leading to the emergence of smart agriculture. This thesis explores the application of computer vision, a subfield of AI, to improve various agricultural activities. Computer vision technologies enable the analysis of visual data captured by drones, satellites and ground sensors to provide real-time information on crop health, soil conditions, pest infestations and overall management of agricultural operations. By leveraging advanced image processing and deep learning techniques, these systems can accurately detect diseases, monitor crop growth, estimate yields, and even automate machines for precision agriculture. This research highlights the potential of computer vision to optimize resource use, reduce environmental impact and increase agricultural productivity and sustainability. Additionally, it addresses challenges associated with implementing these technologies, such as data variability and the need for high-quality annotated datasets, and offers potential solutions to overcome these obstacles. The findings of this thesis highlight the transformative impact of AI-powered computer vision in promoting innovation and efficiency in modern agriculture, contributing to global food security and sustainable agricultural practices .
Artificial intelligence, Sustainable development, Computer vision, Smart agriculture, Image processing, Deep learning, Machine learning, Agricultural data analysis
The candidate must meet the following criteria:
- Academic training :
- Master's degree in Artificial Intelligence, Computer Science, or a related field: A solid background in AI and computer science is essential to understand and develop complex models.
- Knowledge of Agronomy or Agricultural Sciences: A basic understanding of agricultural principles and the challenges encountered in this field is a major asset (preferred).
- Technical skills :
- Programming Skills: Proficiency in programming languages such as Python, C, C++, MATLAB or Java. Experience with computer vision libraries like OpenCV, TensorFlow, Keras, or PyTorch is crucial.
- Computer Vision Algorithms: In-depth knowledge of image processing, object detection and recognition, and image segmentation techniques.
- Machine and Deep Learning: Experience designing, training and evaluating machine and deep learning models, including convolutional neural networks (CNN).
- Data Analysis Skills:
- Data Processing and Analysis
- Statistics and Mathematics
- Passion and Motivation:
Interest in Agriculture and Technological Innovation: A passion for improving agricultural practices through technological innovation and a motivation to contribute to sustainable solutions for agriculture.
Thesis director : EL MOUHTADI Meryam
Please send the application before September 30, 2024 to:
Cedoc.admission@ueuromed.org & m.el-mouhtadi@emida.ueuromed.org