TRANSFER LEARNING APPLIED TO PRECISION AGRICULTURE

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.