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• The performance of 40 combinations for vegetation segmentation was assessed. • A clear difference in performance was observed among various combinations under the given conditions. • CIVE + Kapur showed the best performance; while VEG + Kapur showed the worst. • When adapting the combination to the given conditions yielded a slightly higher performance. • However, the expected advantage of adapting the combination to the field condition is not large. 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. [ABSTRACT FROM AUTHOR]