In weed control the aims of securing crop productivity and protecting biodiversity are often difficult to reconcile. Currently, the development of autonomous in-field intervention technology, such as field robots, is creating new potential for minimizing trade-offs between these two aims. To exploit this potential, weed management strategies need to adapt. However, it is currently unclear which kind of input information (e.g. weed cover, number of weeds, weed species identity) is required for such a targeted approach, and which impacts the robotic application has on the trade-off between crop yield and biodiversity. Here, we used a dataset from organically farmed fields to assess several weed management strategies, simulating robot-supported weed control. Specifically, we used within-field heterogeneity of several weed and crop productivity variables to model effects of different kinds of input information for a hypothetical, spatially selective robotic weed control system. The results showed that, at a defined yield loss, gamma diversity (number of weed species on the entire investigated area) is maintainable to a large degree, even without information on weed or crop heterogeneity within the field being used to decide where to weed. However, to maintain alpha diversity (average number of weed species per plot), more spatially explicit input information is required, such as on the number of species per plot, weed quantity (weed cover per species), and weed competitiveness. Consequently, a weeding robot would have to be technically capable of distinguishing between individual weed species, measuring weed cover, processing captured information in real time and removing weeds at per-plant level. Further, it could be shown that the success of such a complex weed management strategy is independent of the degree of spatial heterogeneity of crop yield and of the present level of weed species richness.

在杂草控制中，确保作物生产力和保护生物多样性的目标往往难以协调一致。目前，自主现场干预技术（例如现场机器人）的发展正在为最大限度地减少这两个目标之间的权衡创造新的潜力。为了挖掘这一潜力，杂草管理策略需要进行调整。然而，目前尚不清楚这种有针对性的方法需要哪种输入信息（例如杂草覆盖、杂草数量、杂草物种身份），以及机器人应用对作物产量和生物多样性之间的权衡有何影响。在这里，我们使用有机农田的数据集来评估几种杂草管理策略，模拟机器人支持的杂草控制。具体来说，我们使用几种杂草和作物生产力变量的田间异质性来模拟假设的空间选择性机器人杂草控制系统的不同类型输入信息的影响。结果表明，在确定的产量损失下，即使没有田间杂草或作物异质性信息来决定除草地点，伽玛多样性（整个研究区域的杂草种类数量）在很大程度上也是可以维持的。然而，为了保持阿尔法多样性（每个地块的杂草物种的平均数量），需要更明确的空间输入信息，例如每个地块的物种数量、杂草数量（每个物种的杂草覆盖度）和杂草竞争力。因此，除草机器人在技术上必须能够区分单个杂草种类、测量杂草覆盖率、实时处理捕获的信息并在每株植物的水平上清除杂草。此外，还可以证明，这种复杂的杂草管理策略的成功与作物产量的空间异质性程度和杂草物种丰富度的当前水平无关。