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Using AI to Improve Crop Tracking, Soil Investigation, and Nutrient Management in Precision Agriculture

Using AI to Improve Crop Tracking, Soil Investigation, and Nutrient Management in Precision Agriculture
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Author(s): M Balasubramani (V.S.B. Engineering College, India), M. P. Rajakumar (St. Joseph's College of Engineering, India), R. Suganthi (Dr. N.G.P. Arts and Science College, India), M. Navaneetha Krishnan (St. Joseph College of Engineering, India), K. Raghavi (KCG College of Technology, India)and M. Robinson Joel (KCG College of Technology, India)
Copyright: 2026
Pages: 26
Source title: Precision and Intelligence in Agriculture: Advanced Technologies for Sustainable Farming
Source Author(s)/Editor(s): Pawan Whig (Vivekananda Institute of Professional Studies, India)and Ahmed Elngar (Beni-Suef University, Egypt)
DOI: 10.4018/979-8-3373-5283-1.ch009

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Abstract

Artificial intelligence (AI) is transforming precision agriculture by enhancing soil analysis, crop tracking, and fertilizer management. AI-powered systems enable real-time monitoring of crop health, soil nutrient levels, and environmental conditions, providing farmers with actionable insights. Machine learning algorithms integrated with remote sensing technologies, such as drones and satellites, help monitor crops, predict yields, and detect early stress signs. In soil research, AI combines data from IoT sensors, GIS, and soil samples to create detailed soil health maps and predict changes. Predictive analytics optimize nutrient management by recommending precise application of water, fertilizers, and amendments based on real-time crop and soil needs. Automation, decision support systems (DSS), and variable rate technology improve input efficiency, reducing waste and environmental impact. By leveraging AI in these areas, precision agriculture promotes sustainable farming, lowers costs, and increases productivity through improved data analysis, real-time monitoring, and automation.

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