SMART AGRICULTURE SYSTEM FOR SUSTAINABLE DEVELOPMENT: METHODS AND PRACTICES

Abstract

Modern farming is being transformed by smart agricultural systems, which use creative innovation for long-term sustainability. Examining the approaches of smart agriculture, this paper mostly focuses on precision farming, IoT integration, and data-driven decision-making procedures. Precision farming's toolkit consists of drones, sensors, and global positioning systems (GPS), meant to enhance crop management, efficiency, and quality of harvest. By means of IoT, agricultural processes may be automated and real-time monitored, thus improving water and energy efficiency, lowering environmental effect, and raising productivity. Using predictive insights made available by merging big data analytics with machine learning algorithms helps one better forecast and address issues like global warming, pest control, and soil health. This research examines case studies and pilot projects all over to better grasp what factors influence the scalability and acceptability of an implementation as well as what makes it successful. The paper contends that infrastructural development, farmer education, and supportive laws are vital if smart agriculture is to fully realize itself. It addresses the social and financial consequences as well as how it may improve food security, provide employment, and increase farmers' pay. Emphasizing the need of research and development expenditure, stakeholder involvement, and continuous innovation, the paper closes with a conversation about what the future holds. By the use of smart agriculture systems, which mix contemporary technology with time-honoured farming practices, sustainable agricultural development—including the protection of the environment and future generations' food supply—may be much strengthened.