Abstract Robust vegetation segmentation is required for a vision-based weed control robot in an agricultural field operation. The output of vegetation segmentation is a fundamental element in the subsequent process of weed/crop discrimination as well as weed control actuation. Given the abundance of colour indices and thresholding techniques, it is still far from clear how to choose a proper threshold technique in combination with a colour index for vegetation segmentation under agricultural field conditions. In this research, the performance of 40 combinations of eight colour indices and five thresholding techniques found in the literature was assessed to identify which combination works the best given varying field conditions in terms of illumination intensity, shadow presence and plant size. It was also assessed whether it was better to use one specific combination at all times or whether the combination should be adapted to the field conditions at hand. A clear difference in performance, represented in terms of MA (Modified Accuracy) which indicates the harmonic mean of relative vegetation area error and balanced accuracy, was observed among various combinations under the given conditions. On the image dataset that was used in this study, CIVE+Kapur (Colour Index of Vegetation Extraction+Max Entropy threshold) showed the best performance while VEG+Kapur (Vegetative Index+Max Entropy threshold) showed the worst. Adapting the combination to the given conditions yielded a slightly higher performance than when using a single combination for all (in this case CIVE+Kapur). Consistent results were obtained when validated on a different independent image dataset. Although a slightly higher performance was achieved when adapting the combination to the field conditions, this slight improvement seems not to outweigh the potential investment in sensor technology and software that are needed in practice to accurately determine the different conditions in the field. Therefore, the expected advantage of adapting the combination to the field condition is not large.

中文翻译：

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甜菜志愿马铃薯防治植被分割中颜色指数与阈值技术组合的研究

摘要 基于视觉的杂草控制机器人在农田作业中需要稳健的植被分割。植被分割的输出是后续杂草/作物识别以及杂草控制驱动过程中的基本要素。鉴于颜色指数和阈值技术的丰富性，如何选择合适的阈值技术结合颜色指数进行农田条件下的植被分割还远未明确。在这项研究中，评估了文献中发现的 8 种颜色指数和 5 种阈值技术的 40 种组合的性能，以确定在光照强度、阴影存在和植物大小方面的不同现场条件下，哪种组合最有效。还评估了是否始终使用一种特定组合更好，或者该组合是否应该适应手头的田间条件。在给定条件下的各种组合之间观察到性能的明显差异，以 MA（修正精度）表示，它表示相对植被面积误差和平衡精度的调和平均值。在本研究中使用的图像数据集上，CIVE+Kapur（植被提取颜色指数+最大熵阈值）表现最好，而 VEG+Kapur（植物指数+最大熵阈值）表现最差。使组合适应给定条件比使用单一组合（在本例中为 CIVE+Kapur）时的性能略高。在不同的独立图像数据集上进行验证时获得了一致的结果。尽管在使组合适应现场条件时实现了略高的性能，但这种微小的改进似乎并没有超过在实践中准确确定现场不同条件所需的传感器技术和软件的潜在投资。因此，使组合适应现场条件的预期优势不大。