Optimizing Agricultural Practices with Machine Learning Techniques
Ruhiya Taj
student, Dept of CSE,
Sea College of Engineering & Technology
Saniya
student, Dept of CSE,
Sea College of Engineering & Technology
Umme kulsum
student, Dept of CSE,
Sea College of Engineering & Technology
Vellena Ningthoujam
student, Dept of CSE,
Sea College of Engineering & Technology
Mrs ranjani devi M
Assistant Professor Dept of CSE
SEA College of Engineering & Technology
Mr.Nagabhiravnath K
Assistant Professor Dept of CSE
SEA College of Engineering & Technology
Mrs Jayashri M
Assistant Professor Dept of CSE
SEA College of Engineering & Technology
Dr Krishna Kumar P R
Professor Dept of CSE
SEA College of Engineering & Technology
Abstract:
The rapid advancement of data science and artificial intelligence has opened new frontiers in modern agriculture, enabling smarter and more efficient farming practices. This study explores the application of machine learning (ML) techniques to optimize various aspects of agricultural operations, including crop yield prediction, soil health monitoring, pest and disease detection, irrigation management, and precision farming. By leveraging historical data, sensor outputs, satellite imagery, and environmental variables, ML models can identify patterns and make data-driven decisions that enhance productivity while reducing resource consumption. This paper reviews state-of-the-art machine learning algorithms such as decision trees, support vector machines, random forests, and neural networks, evaluating their performance in different agricultural use cases. Additionally, it discusses the integration of ML with IoT devices and remote sensing technologies to create intelligent, automated systems for real-time agricultural monitoring. The results demonstrate that ML-driven approaches significantly improve decision-making accuracy and sustainability in farming, offering a transformative potential for the agricultural industry in the era of digitalization.
