Bridging the gap for sustainable crop cultivation using data science applications

Agriculture has long grappled with challenges ranging from unpredictable weather patterns and resource scarcity to crop diseases and yield fluctuations. However, the advent of data science and smart agriculture has introduced innovative solutions to address these longstanding issues. This paper examines how data-driven approaches, combined with technological advancements, have revolutionized agriculture. By integrating sensor networks, satellite imagery, and IoT devices, farmers can gather real-time data on soil quality, weather conditions, and crop health. These data are then analyzed using sophisticated algorithms, allowing for precise decision-making in irrigation, fertilization, and pest control. This study explores a three-tier smart agriculture system using LoRa technology for seamless data transmission. The performance is evaluated against existing frameworks, considering factors like cost and latency. This approach has the potential to revolutionize rural farming, aligning with global trends while addressing specific challenges faced by farmers. Smart agriculture solutions encompass deploying IoT-based sensors for real-time data collection, optimizing crop management and resource allocation. This contributes to the agricultural field by bridging the gap between data science and agriculture, offering a practical solution to enhance crop yield prediction accuracy. The integration of data science in agriculture has the potential to transform the sector by enhancing productivity and sustainability. By enabling informed decision-making, our approach has the potential to optimize resource allocation, reduce waste, and ultimately contribute to global food security in the face of mounting challenges.