A study on applications of artificial intelligence in precision farming

Abstract

Precision farming, a data-driven approach to agriculture, leverages Artificial Intelligence (AI) to enhance efficiency, sustainability, and productivity. AI technologies, including machine learning, computer vision, and the Internet of Things (IoT), play a crucial role in real-time monitoring, predictive analytics, and automated decision-making. AI-driven systems analyze vast datasets from satellite imagery, drones, and soil sensors to optimize irrigation, fertilization, and pest control, reducing resource wastage while maximizing crop yields. The integration of AI-powered autonomous machinery and robotics further enhances precision, enabling targeted interventions with minimal human intervention. Machine learning models assist in disease prediction and early detection, allowing farmers to take preventive measures, thereby minimizing crop losses. Additionally, AI-powered climate forecasting helps mitigate risks associated with unpredictable weather patterns, improving resilience in agriculture. Technologies such as deep learning aid in weed detection and precision spraying, reducing the excessive use of herbicides and promoting environmental sustainability. Recent advancements in AI-powered decision-support systems offer tailored recommendations based on historical and real-time farm data, ensuring resource-efficient, cost-effective, and sustainable farming practices. The integration of AI in precision agriculture not only enhances crop quality and productivity but also promotes sustainable farming methods, reducing environmental degradation. As AI continues to evolve, its role in precision farming will be crucial in addressing global food security challenges and fostering a more resilient agricultural ecosystem.