Using Smart Drones to Monitor Fertilization and Plant Health
Keywords:
Precision Farming, Edge Computing, Smart Drones (UAV), Variable Rate Application, Convolutional Neural NetworkAbstract
Monitoring plant health and managing fertilization using Unmanned Aerial Vehicles (UAVs) currently still rely heavily on post-flight processing architecture and cloud computing, which causes high latency issues and inefficiency in agronomic interventions in areas without internet connectivity. This study aims to develop an intelligent drone system based on edge computing to diagnose the biological status of plants and independently formulate precise fertilization dose estimates instantly, right in the air. Using an experimental hardware engineering design (R&D) approach, this research integrates multispectral sensors (Near-Infrared and Red-Edge) with a GPU-accelerated Single Board Computer on a multi-rotor platform.A lightweight Convolutional Neural Network (CNN) algorithm is quantized and embedded into a microcontroller to autonomously execute Variable Rate Application (VRA) maps. Agronomic accuracy is validated using controlled field tests with SPAD meter calibration. Architecture evaluation shows that edge computing successfully reduces on-board inference latency to under 0.5 seconds per frame. The predictive model achieves a classification precision (mAP) exceeding 90% with a regression correlation (R²) > 0.85 compared to ground truth, which, in simulations, can reduce nitrogen chemical fertilizer usage by up to 30%. Edge-AI integration has proven effective in overcoming data transmission constraints in precision agriculture systems, enabling real-time fertilization actions.Full computing placement on vehicles is very relevant for rural ecosystems. Further research is recommended to study passive thermal management engineering to prevent thermal throttling in edge computing under extreme tropical climates.
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