Leveraging Predictive Analytics and Smart Irrigation for Sustainable Agriculture: A Systematic Literature Review
Sahana J, Rakesh H C, Pranav H Ravi, Vijesh H D, Nivyashree R Computer Science and Engineering, Malnad College of Engineering Hassan-573202, India
Email: {Sahanajagdeesh4@gmail.com, rakeshchandru21@gmail.com, pranavhravi@gmail.com, vijeshhd@gmail.com, rns@mcehassan.ac.in}
Abstract—Agriculture remains a cornerstone of human civi- lization, providing essential resources such as food, fiber, and fuel. However, the sector faces numerous challenges, particularly for small-scale farmers who are disproportionately affected by resource inefficiencies, climate variability, and soil degradation. These challenges have resulted in declining crop yields, unsustain- able resource use, and increased vulnerability to climate change. Addressing these issues requires a multi-faceted approach that combines technological innovation with practical, data-driven solutions tailored to the needs of small-scale farmers.
This research introduces a comprehensive, technology-driven system that integrates predictive analytics, smart irrigation, and soil health monitoring to enhance agricultural sustainability. By leveraging real-time data from IoT devices, weather forecasts, and machine learning algorithms, the system provides actionable insights for optimizing crop growth, water usage, and soil health. Predictive crop growth models empower farmers to make informed decisions, while the smart irrigation system delivers precise water management based on soil moisture levels and environmental conditions. Additionally, the soil health monitoring module generates data-driven recommendations for nutrient management and sustainable farming practices. Through this holistic approach, the research aims to mitigate resource inef- ficiencies, boost agricultural productivity, and ensure long-term resilience for small-scale farming communities.
Index Terms—agriculture sustainability, predictive analytics, smart irrigation, soil health monitoring, IoT in farming, machine learning in agriculture, resource efficiency, small-scale farming, climate-resilient agriculture, precision farming
