Unmanned Aerial Vehicles and Low-Cost Sensors for Monitoring Biophysical Parameters of Sugarcane

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with low-cost RGB and near-infrared (NIR) cameras represent efficient and scalable technology for monitoring sugarcane crops. This study evaluated the potential of UAV imagery and three-dimensional crop modeling to estimate sugarcane height and yield under different nitrogen fertilization levels. The experiment comprised 28 plots subjected to four nitrogen rates, and images were processed using a Structure from Motion (SfM) algorithm to generate Digital Surface Models (DSMs). Crop Height Models (CHMs) were obtained by subtracting DSMs from Digital Terrain Models (DTMs). The most accurate CHM was derived from the combination of the reference DTM and the NIR-based DSM (R2 = 0.957; RMSE = 0.162 m), while the strongest correlation between height and yield was observed at 200 days after cutting (R2 = 0.725; RMSE = 4.85 t ha−1). The NIR-modified sensor, developed at a total cost of USD 61.59, demonstrated performance comparable with commercial systems that are up to two hundred times more expensive. These results demonstrate that the proposed low-cost NIR sensor provides accurate, reliable, and accessible data for three-dimensional modeling of sugarcane.

Code and data availability

The supplied blocks describe UAV RGB/NIR imagery, SfM-derived DSM/DTM/CHM products, field height measurements, and yield data for a sugarcane experiment, but contain no data availability statement, repository deposit, or author code/model release. No paper-specific public asset is identified.

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