Integration of Bangla voice-controlled farming robot with convolutional neural networks for smart agricultural applications

Abstract

This project develops AGRIBOT, a low-cost smart farming robot that combines Bengali/English voice interaction, artificial intelligence, computer vision, and robotics to support farmers in Bangladesh. The robot uses CNN-based mango leaf disease detection and performs basic agricultural tasks such as irrigation and spraying. Built with Raspberry Pi, Arduino, sensors, motors, and pumps, the system integrates Python, TensorFlow, OpenCV, NLP, and MySQL. A human-centered design approach, including farmer surveys and interviews, was used to understand user needs. The study demonstrates the potential of voice-enabled AI agricultural robots to improve access to farming information, support timely disease detection, and promote sustainable and inclusive agriculture, while identifying challenges such as limited datasets, hardware durability, and environmental noise.