| Abstract: | Precision agriculture is a critical component for enhancing food security and economic
stability in Algeria. However, its implementation is significantly hampered by challenges
unique to the region, including the scarcity of labeled agricultural data and high environmental
variability. These obstacles limit the effectiveness of traditional data-intensive
deep learning models. This thesis provides a comprehensive review of advanced, dataefficient
artificial intelligence techniques that hold the potential to overcome these limitations.
The primary objective of this work is to establish a thorough understanding of the
current state-of-the-art by surveying the literature on Transfer Learning, Vision Foundation
Models (VFMs), and Few-Shot Learning (FSL) as applied to agricultural remote
sensing. The study begins by establishing the general context of Algerian agriculture
and its governing bodies, followed by an in-depth analysis of the theoretical foundations
of key AI architectures, including Convolutional Neural Networks (CNNs) and Vision
Transformers (ViTs).
The literature review systematically examines existing research, categorizing studies
by their focus on remote sensing applications, agricultural vision tasks, and the use of
foundation models. The analysis reveals a significant research gap in the application of
large-scale, pre-trained VFMs combined with data-efficient fine-tuning strategies specifically
tailored for the heterogeneous and data-scarce Algerian agricultural landscape. This
thesis concludes by synthesizing these findings to highlight the most promising research
directions for developing robust, scalable, and locally-attuned precision agriculture solutions
in Algeria and similar regions. |
