AgroFarmIQ: Advanced Crop Monitoring and Yield Prediction

AgroFarmIQ: Advanced Crop Monitoring and Yield Prediction

Dr. R. Kanagavalli , Professor and HOD , Department of Information Science and Engineering ,The Oxford College of Engineering

    Swapnil S, Student , Department of Information Science and Engineering ,The Oxford College of Engineering

    Shrikaran KS, Student , Department of Information Science and Engineering ,The Oxford College of Engineering

    Syed Sulaiman Zamani , Student , Department of Information Science and Engineering ,The Oxford College of Engineering

Abstract - In response to the imperatives of modern agriculture, our project presents a comprehensive precision agriculture solution centered around a multifunctional robocar. This autonomous vehicle integrates IoT sensors for data-driven crop management, employing machine learning for monitoring and predicting yields while optimizing resource utilization. Automation features include automated obstacle avoidance, periodic image capture, and automatic water spraying for real-time data collection and management, empowering farmers with actionable insights for informed decision-making. Additionally, a web-based platform utilizing Streamlit framework facilitates rapid and accurate crop disease detection using a meticulously trained VGG19 model from uploaded images. By embracing advanced technology, our solution aims to enhance agricultural productivity and pave the way for a sustainable future.

Keyword: Precision agriculture, Robocar, IoT sensors, Machine learning, Crop management, Automated farming, Data-driven agriculture, Resource optimization, Real-time monitoring, Sustainable farming