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.

识别甘蔗行内的差距是使用特定地点的方法优化输入的有效策略。这项工作旨在比较四种不同的基于传感器的技术来识别和测量甘蔗间隙。 具体来说，分析了三种策略，其中传感器（植物指数、超声波、光电）安装在拖拉机上，一种策略使用车载 RGB 摄像头遥控飞机（RPA）；后者是目前的商业方法。在四个作物发育阶段进行了田间试验：收获后 19、31、47 和 59 天 (DAH)。成功的差距识别通过准确性、精确度和召回率进行评估。我们使用均方根误差 (RMSE) 来评估传感器测量间隙长度。所有基于传感器的技术在识别 31 和 47 DAH 的差距方面的准确度都在 80% 到 92% 之间。在 19 DAH，基于传感器的方法高估了差距的数量，而在 59 DAH，则低估了差距。在甘蔗发育阶段，光电传感器在测量间隙长度（RMSE ≤ 0.18 m）方面性能最佳，RMSE 变化最小。植物指数传感器 (VIS) 和 RPA 图像具有相似的性能，RMSE 范围在 0.11 和 0.40 m 之间。47 和 59 DAH 中植物的冠层影响了这两种方法。植物冠层越大，它们识别和测量间隙的能力就越低。在甘蔗发育阶段，光电传感器在测量间隙长度（RMSE ≤ 0.18 m）方面性能最佳，RMSE 变化最小。植物指数传感器 (VIS) 和 RPA 图像具有相似的性能，RMSE 范围在 0.11 和 0.40 m 之间。47 和 59 DAH 中植物的冠层影响了这两种方法。植物冠层越大，它们识别和测量间隙的能力就越低。在甘蔗发育阶段，光电传感器在测量间隙长度（RMSE ≤ 0.18 m）方面性能最佳，RMSE 变化最小。植物指数传感器 (VIS) 和 RPA 图像具有相似的性能，RMSE 范围在 0.11 和 0.40 m 之间。47 和 59 DAH 中植物的冠层影响了这两种方法。植物冠层越大，它们识别和测量间隙的能力就越低。