Authors:
Cheikhou Kane
1
and
Pascal Faye
2
Affiliations:
1
Université Rose Dieng France-Sénégal, Dakar, Senegal
;
2
Department of Mathematics and Computer Science, Université du Sine Saloum El Hadj Ibrahima NIASS, Kaolack, Senegal
Keyword(s):
Digital Farming, Machine Learning, Distributed Control.
Abstract:
This paper introduces Secure Digital Farming, a comprehensive approach to enhancing farm security and optimizing crop yield. SDF addresses critical challenges faced by modern agriculture, including climate change, pest control, rural crime, and demographic pressures, all of which threaten agricultural perimeters and impact yield. Our SDF-based solution leverages deep-learning algorithms to analyze sensor data and video streams from security cameras, enabling intelligent access control, pest detection, and yield estimation. This paper outlines the implementation framework for SDF, highlighting its feasibility for real-life testing and validation. We plan to conduct field tests on our educational farm in the peanut basin of Senegal to evaluate the efficacy and practicality of SDF in a real-world setting.