Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches

Identifying gaps within sugarcane rows is an effective strategy to optimise inputs using site-specific approaches. This work aimed to compare four different sensor-based techniques to identify and measure sugarcane gaps. Specifically, it was analysed three strategies with sensors (vegetative index, ultrasonic, photoelectric) mounted on a tractor, and one strategy using an RGB camera on-boarded to a remotely piloted aircraft (RPA); the latter being the current commercial method. Field trials were performed during four crop stages of development: 19, 31, 47, and 59 days after harvest (DAH). The successful gap identification was evaluated by accuracy, precision, and recall. We use the root-mean-square error (RMSE) to evaluate sensors in measuring the length of the gaps. All sensor-based techniques had accuracy between 80% and 92% in identifying the gaps at 31 and 47 DAH. At 19 DAH, the sensor-based methods overestimate the number of gaps, and at 59 DAH, there was an underestimation of gaps. The photoelectric sensor has the best performance in measuring the length of the gaps (RMSE < 0.18 m) with the least variation in RMSE over the stage of sugarcane development. The vegetative index sensor (VIS) and RPA images had similar performance, with the RMSE ranging between 0.11 and 0.40 m. The canopy of the plants in the 47 and 59 DAH affected these two methodologies. The larger is the plant canopy, the lower is their ability to identify and measure the gaps. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study compared four different sensor-based techniques to identify and measure sugarcane gaps, with differences in accuracy and RMSE performance. The ultrasonic sensor had the best performance in measuring gap length.