Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images

Abstract

The use of unmanned aerial vehicles (UAVs) as remote sensing platforms has tremendous potential for describing detailed site-specific features of crops, especially in early post-emergence, which was not possible previously with satellite images. This article describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. The procedure consists of three consecutive phases: (1) identification of sugarcane planting rows, (2) identification of the existent sugarcane within the crop rows, and (3) skip extraction and creation of field-extent crop maps. Results based on experimental fields achieved skip rates of between 2.29% and 10.66%, indicating a planting operation with excellent and good quality, respectively. The relationship of estimated versus observed skip length had a coefficient of determination of 0.97, which was confirmed by the value of the enhanced Wilmott concordance coefficient of 0.92, indicating good agreement. The OBIA procedure allowed a high level of automation and adaptability, and it provided useful information for decision making, agricultural monitoring, and the reduction of operational costs.