AI Based Smart Agriculture Monitoring System

Authors: Mrs. R. Rajavaishnavi, V. Anand Kumar, U. Vamsi Reddy, T. Venkat Upendra Babu

Abstract: Smart Agriculture is transforming the conventional farming ecosystem through the integration of Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing technologies. This paper presents the design and implementation of an AI-Based Smart Agriculture Monitoring System that continuously monitors critical agricultural parameters such as soil moisture, temperature, humidity, pH levels, and crop health using a distributed IoT sensor network. The collected sensor data is processed using advanced machine learning algorithms including Random Forest, LSTM networks, and MobileNetV2-based CNN models to deliver real-time insights and predictive analytics to farmers. The system computes unit-wise crop coverage weightage and allows flexibility through Auto Weightage, Equal Weightage, and Custom Weightage options. It also allows the inclusion of Bloom's Taxonomy cognitive levels to ensure balanced assessment of crop monitoring objectives. Finally, the generated monitoring report is formatted as per the required agricultural pattern and exported as a PDF file for field use.