Mini Drone-Based Precision Agriculture for Indonesian MSMEs: A Low-Cost AI-Assisted Monitoring System
Abstract
This research introduces a cost-effective drone-based agricultural monitoring system targeted at Indonesia’s smallholder farming enterprises (MSMEs). By leveraging mini drones (DJI Mini 2 SE) and lightweight AI models, farmers can segment land, detect vegetation health, and count crops using simple RGB video analysis. The system utilizes a mobile-to-YouTube private livestream pipeline and performs video processing offline using semantic segmentation (U-Net) and object detection (YOLOvX). The prototype system—tested on a 300m² vegetable plot—shows promising results with over 90% detection accuracy and effective land use visualization. The interface, built with Streamlit, provides real-time insights, affordability, and aligns with Smart City goals of accessibility and sustainability.
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