Precision Agriculture Advisor
DOI:
https://doi.org/10.64252/21dyn029Keywords:
Precision agriculture, Crop recommendation system, Soil analysis, Machine learning, Artificial intelligence, Yield prediction, Soil type classification, Farming optimization, Data-driven farming, Agritech solution.Abstract
An AI-driven system has revolutionized crop selection and yield prediction in modern agriculture through the utilization of AI algorithms and IoT technologies. This system integrates IoT sensor data, encompassing pH levels, moisture content, and nutrient composition, to customize crop recommendations based on distinct soil types, environmental conditions, and historical yield patterns. Predictive analytics estimate crop yields by analyzing diverse factors such as weather forecasts and agronomic indicators. The system ensures precision in decision-making by incorporating key components like data acquisition, preprocessing modules, and a user-friendly interface for real-time monitoring. Leveraging a Random Forest algorithm, the system attains outstanding performance metrics, boasting a precision rate of 99.7%, an accuracy rate of 99.75%, and a specificity rate of 98.7%. Real-world validation has demonstrated enhanced crop productivity and profitability, offering substantial potential for sustainable farming practices and food security.
