Proceeding ICSEMT Aug 2026

A Systematic Review Of Deep Learning Approaches For Agricultural Monitoring Using Satellite Imagery

Authors: Vandana Birle, Dr. Dilip Singh Solanki

Abstract: The increasing demand for sustainable agricultural production has accelerated the adoption of advanced technologies for crop monitoring and farm management. Satellite imagery has emerged as a valuable source of large-scale and continuous agricultural data, while deep learning techniques have demonstrated remarkable capabilities in extracting meaningful information from complex remote sensing datasets. This systematic review examines recent advances in deep learning approaches applied to satellite imagery for agricultural monitoring. The review analyzes studies published between 2023 and 2026 focusing on crop classification, farmland segmentation, crop health assessment, disease detection, yield prediction, land-use mapping, and environmental stress monitoring. Various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), and hybrid models, are discussed in relation to their effectiveness in agricultural applications. The findings indicate that deep learning significantly improves the accuracy and efficiency of agricultural monitoring compared to conventional image processing methods. However, challenges related to data availability, model generalization, computational complexity, and real-time implementation remain significant barriers to widespread adoption. The review highlights emerging trends and identifies future research directions for developing intelligent, scalable, and sustainable agricultural monitoring systems through the integration of satellite imagery and artificial intelligence.

DOI: http://doi.org/10.5281/zenodo.21412141