An Analysis of Satellite Imagery for Crop Production using Deep Learning Techniques

DOI

10.2991/978-94-6463-718-2_12How to use a DOI?

Keywords

Precision Agriculture; Satellite Imagery; Machine Learning; Deep Learning; Weed Detection; Seeding Schemes; Water Body Management

Abstract

A research project aimed at ascertaining how the most advanced technologies are integrated into enhancing agricultural productivity and sustainability is conducted. The study utilized satellite imagery to analyze key factors determining crop production that can be condensed into three key areas, namely: Weed detection, seeding and planting scheme and water body detection. This improvement in the accuracy and efficiency of the identification of areas overgrown and infested by weeds within agricultural fields is done with the use of CNN. High-resolution satellite images are thus being processed for the proper delineation of distinct zones of vegetation such that interventions can be made much more specific through better crop management practices. Another major study that could be conducted is optimizing the planting and sowing strategy. Using machine learning algorithms applied over data from satellites combined with planting patterns and densities, there would maximized crop yields and optimized land use for better seed and plant selection towards more efficient and productive agriculture. It focuses on water body management wherein images obtained from satellites are applied for the control and monitoring purpose of water use. It identifies and classifies maintained and non-maintained water bodies, analyzing their nature of distribution and availability. This helps in finding a sustainable strategy for the management of water, which then forms an important cause for sufficient water supply during the growing season. All these technologies, and several more, uncover a lot of potential in elevating agricultural productivity by providing actionable insights to the farmers and farm managers. Bringing this conclusion to culmination, the study is going to make agriculture more precise, efficient, and even more profitable by using deep learning algorithms in analyzing satellite imagery.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Thamizhamuthu
AU  - M. Revathi
AU  - Vartika Jaiswal
AU  - Parul Kohli
PY  - 2025
DA  - 2025/05/23
TI  - An Analysis of Satellite Imagery for Crop Production using Deep Learning Techniques
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 125
EP  - 137
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_12
DO  - 10.2991/978-94-6463-718-2_12
ID  - Thamizhamuthu2025
ER  -