Optimizing Sustainable Cultivation Through Smart Irrigation

Abstract

This paper presents a comprehensive study of a predictive irrigation system, an innovative approach in smart agriculture focusing on integrated irrigation management through advanced predictive techniques. Employing a blend of Internet of things (IoT) technology, Machine Learning (ML) algorithms, and data analytics, this system marks significant improvements in agricultural irrigation strategies. It is designed to optimize water use, improve crop yields, and promote sustainable farming practices in the face of evolving environmental challenges. The paper outlines the system's architecture, including the deployment of IoT sensors for continuous data collection, the integration of ML models for predictive analysis, and the implementation of adaptive irrigation scheduling algorithms. A detailed examination in a study case of the system's performance reveals substantial improvements in water usage efficiency compared to traditional irrigation methods. Additionally, the paper discusses the challenges and limitations encountered, such as the high initial setup costs, technical complexities, and the necessity for continuous data accuracy. The study concludes by underscoring the Irrigation predictive system's potential in transforming agricultural practices. It highlights its role in enhancing resource management and sustainability in farming, while also pointing out the areas for future research to further refine of system for wider applicability.

Keywords

• Smart agriculture
• Predictive irrigation system
• Irrigation predictive system
• Internet of Things
• Machine Learning
• Sustainability.