Development of Smart Crop Monitoring Systems Using Artificial Intelligence and Sensor Networks

Abstract

The increasing need for real-time and precise monitoring of crop conditions has driven the adoption of intelligent technologies in modern agriculture. This study presents the development of a smart crop monitoring system using artificial intelligence integrated with sensor networks to enhance crop management and productivity. The proposed methodology incorporates multi-source data collected from distributed in-field sensors, including soil moisture, temperature, humidity, light intensity, and nutrient levels, along with environmental and crop health indicators. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure accuracy and consistency. Advanced machine learning and deep learning models, such as Random Forest, Support Vector Machines, Convolutional Neural Networks, and Artificial Neural Networks, are employed to analyze complex patterns and provide predictive insights into crop growth, stress conditions, and environmental variations. The system is designed with real-time data acquisition and adaptive learning capabilities, enabling continuous monitoring and dynamic response to changing field conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic zones and crop types. Experimental results demonstrate that the proposed approach significantly improves monitoring accuracy, early detection of anomalies, and decision-making efficiency compared to conventional methods. Additionally, the system provides actionable recommendations for irrigation, fertilization, and pest management. The findings highlight the effectiveness of integrating artificial intelligence with sensor networks for precision agriculture applications. The study concludes that smart crop monitoring systems offer a scalable, efficient, and data-driven solution for optimizing farm management, enhancing crop productivity, and promoting sustainable agricultural practices.