EDGE AI BASED YIELD PREDICTION SYSTEM

EDGE AI BASED YIELD PREDICTION SYSTEM

Manasa P 1, Dr. Geetha M 2

[1] Student, Department of MCA, BIET, Davangere.

[2] Associate Professor, Department of MCA, BIET, Davangere.

ABSTRACT

Agricultural productivity is pivotal for global food security, yet predicting crop yield accurately remains a challenge due to the variability of environmental factors. Traditional cloud-based AI systems, while powerful, often suffer from latency issues and require continuous internet connectivity, which can be a limitation in remote farming areas. This paper presents an Edge AI- based yield prediction system designed to overcome these challenges by processing data locally on- site, thereby offering real-time insights and recommendations to farmers. Our system integrates various data collection devices, including soil sensors, drones, and weather stations, with edge computing hardware such as NVIDIA Jetson and Google Coral. Our research indicates that Edge AI has the potential to transform modern farming, making advanced predictive analytics accessible even in the most remote locations.

Keywords: Edge AI, Yield Prediction, Precision Agriculture, Real-time Data Processing, Machine Learning, Deep Learning.