AI In Precision Agriculture: Real-World Application for Crop Optimization

Authors: Ravi Kumar Perumallapalli

Country: USA

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Abstract: Modern farming has been transformed by precision agriculture, which maximizes crop yield by utilizing cutting-edge technologies like artificial intelligence (AI). Artificial intelligence (AI) systems analyses massive volumes of data from sensors, satellite imagery, and other sources to determine weather patterns, crop health, and soil conditions. The practical applications of AI in precision agriculture are discussed in this article, with a focus on crop optimization through predictive analytics, automated equipment, and resource management. Through a detailed evaluation of architecture, implementation, and outcome analysis, the research shows how AI may change agricultural operations to ensure sustainability and increase yields. Artificial intelligence (AI) technology makes it possible to apply pesticides, fertilizers, and water precisely, which minimizes abuse and its detrimental consequences on the environment. Even though climate continues to have an impact on farming conditions globally, artificial intelligence (AI) offers adaptable solutions that can be built by forecasting weather changes and offering last-minute adjustments to farming operations. AI in agriculture is still facing several obstacles, though, including the high cost of equipment and the requirement for a strong data infrastructure in rural areas. The goal of this research is to present a thorough analysis of AI's influence on crop optimization in precision agriculture. It offers in-depth discussions of the architecture.

Keywords: Crop optimization, advanced agricultural technology, automated machinery, artificial intelligence, sustainable farming, and predictive analytics.

Paper Id: 231508

Published On: 2017-01-03

Published In: Volume 5, Issue 1, January-February 2017

Cite This: AI In Precision Agriculture: Real-World Application for Crop Optimization - Ravi Kumar Perumallapalli - IJIRMPS Volume 5, Issue 1, January-February 2017.

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