Abstract: This project presents the design and development of an AI Integrated Mechanical Drying System for Post Harvest Groundnut with Moisture Sensors, aimed at improving the efficiency, accuracy, and quality of the drying process. Traditional drying methods such as open sun drying and conventional hot-air drying are highly dependent on environmental conditions, require significant manual effort, and often result in uneven moisture removal, contamination, and quality degradation.To overcome these limitations, the proposed system integrates Artificial Intelligence (AI), Internet of Things (IoT), and sensor-based automation, with a special focus on moisture sensing technology for precise drying control. The system is built around an ESP32 microcontroller, which collects real-time data from temperature, humidity, load cell, and moisture sensors. The moisture sensor plays a crucial role by directly measuring the moisture content of groundnuts, while the load cell monitors weight reduction to estimate moisture loss.An AI-based control algorithm analyzes the collected data and predicts optimal drying conditions. Based on this analysis, the system automatically controls the heater and blower through relay modules to maintain proper temperature and airflow. The system also incorporates IoT connectivity, enabling remote monitoring and control through mobile applications such as Blynk, allowing users to track drying parameters and receive real-time updates.The integration of moisture sensors significantly enhances drying accuracy by ensuring that the groundnuts reach the desired safe moisture level without over-drying or under-drying. This improves product quality, shelf life, and reduces the risk of fungal contamination. Additionally, the system minimizes energy consumption and reduces human intervention, making it efficient, reliable, and suitable for both small-scale and commercial applications.

Keywords: Artificial intelligence (AI), Internet of Things (IoT), ESP32 microcontroller, mechanical drying system, groundnut drying, moisture monitoring, predictive control, energy efficiency, automation.

Downloads: | DOI: 10.17148/IARJSET.2026.13435