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Review Article



Precision Agriculture using Artificial Intelligence and Robotics

Mostafa Eissa.




Abstract
Cited by 0 Articles

Precision agriculture leverages AI and robotics to empower farmers with data-driven insights, optimizing field management and achieving remarkable progress towards sustainable practices. By collecting and analyzing data from drones, sensors, satellites and weather stations, farmers gain a deep understanding of their crops' health, needs, and surrounding environment. This knowledge unlocks targeted decision-making in irrigation, fertilization, and pest control, minimizing resource waste and environmental impact. Early detection of disease or nutrient deficiencies through AI-powered analysis enables proactive measures, reducing reliance on chemicals and ensuring healthier crops. Precision technologies also promote efficient water management and conservation by precisely applying irrigation based on real-time soil moisture data. Ultimately, this approach minimizes costs, maximizes yield and addresses future challenges like global food demand and land limitations. Investing in AI and robotics unlocks the potential for farmers to analyze vast datasets, further refining resource allocation, minimizing waste, and maximizing output. This innovative approach paves the way for a thriving and sustainable agricultural future, one field at a time. The minireview article explores the application of AI and robotics in precision agriculture. It highlights the benefits of this approach such as increased crop yields, reduced environmental impact, and improved resource management. The article also discusses the challenges associated with implementing AI and robotics in precision agriculture, such as high costs and data privacy concerns. Overall, the review concludes that AI and robotics have the potential to revolutionize agriculture, but there are challenges that need to be addressed before widespread adoption can be achieved.

Key words: Artificial intelligence, Data-driven insights, Drones, Machine learning, Precision agriculture, Resource optimization, Robotics, Sensors, Sustainability, Targeted interventions






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