AI in precision agriculture: A review of technologies for sustainable farming practices

Abstract

Precision agriculture, facilitated by advancements in Artificial Intelligence (AI), has emerged as a transformative paradigm in modern farming. This review comprehensively examines the integration of AI technologies in precision agriculture to enhance sustainability and optimize farming practices. The paper synthesizes recent research and developments in AI applications, covering key areas such as crop monitoring, resource management, decision support systems, and automation. The adoption of AI-driven techniques, including machine learning, computer vision, and sensor technologies, is reshaping traditional farming methods by providing farmers with real-time data and actionable insights. Crop monitoring applications utilize satellite imagery, drones, and ground-based sensors to assess plant health, detect diseases, and optimize irrigation strategies. AI-driven decision support systems empower farmers to make informed choices based on data-driven predictions, weather forecasts, and historical patterns, contributing to resource-efficient practices and minimizing environmental impact. Resource management is a critical aspect of sustainable farming, and AI plays a pivotal role in optimizing the use of water, fertilizers, and pesticides. Smart irrigation systems, enabled by AI algorithms, ensure precise and efficient water distribution, reducing water wastage and promoting water conservation. AI-driven analysis of soil conditions helps farmers tailor fertilization practices, enhancing nutrient utilization and minimizing environmental runoff. The review also explores the role of AI in automating farming operations through robotics and autonomous vehicles. These technologies not only alleviate labor shortages but also improve efficiency in planting, harvesting, and crop maintenance. Additionally, the integration of AI fosters connectivity in agriculture, enabling seamless communication between devices, sensors, and farming equipment. As precision agriculture continues to evolve, the review highlights challenges and future prospects. Ethical considerations, data security, and the digital divide in rural areas are among the challenges that need attention. Moreover, the paper discusses potential avenues for further research, emphasizing the need for interdisciplinary collaboration to address the complex issues associated with the sustainable implementation of AI in precision agriculture. This review provides a comprehensive overview of the transformative impact of AI in precision agriculture, offering insights into current technologies, challenges, and future directions. The integration of AI not only enhances productivity and efficiency but also contributes to the long-term sustainability of farming practices, ensuring food security in the face of a growing global population.