Published Paper Details:

Agriculture is one of the most important sectors for economic growth and food security. However, farmers face many challenges such as unpredictable weather conditions, water scarcity, soil nutrient imbalance, pest attacks, crop diseases, and inefficient use of resources. Traditional farming methods often depend on manual observation and past experience, which may not always provide accurate or timely decisions. As a result, crop productivity can decrease and operational costs may increase. To overcome these issues, this paper presents an AI Based Agriculture Monitoring System that combines Artificial Intelligence (AI), Internet of Things (IoT), environmental sensors, cloud computing, and machine learning techniques for smart farming applications. The proposed system continuously monitors important agricultural parameters such as soil moisture, temperature, humidity, soil pH, and crop leaf condition. Sensors collect real-time data, which is processed using a microcontroller and transmitted to a cloud platform for remote access. Machine learning algorithms are used to analyze the collected data and generate intelligent recommendations. A Convolutional Neural Network (CNN) is used for plant disease detection, while Random Forest and Regression models are used for irrigation planning and crop yield estimation. The system can automatically alert farmers regarding water requirements, disease symptoms, and abnormal environmental conditions. The proposed model aims to improve farming efficiency, reduce water wastage, minimize manual effort, and increase crop productivity. It is low-cost, scalable, and suitable for both small and medium farms. This project demonstrates how interdisciplinary technologies can contribute toward sustainable and modern agriculture.