Nitrogen (N) excess due to mineral fertilization in conventional crop farming has a significant negative impact on the environment. Variable rate N application (VRNA) is a promising tool to increase N recovery rates in spatially heterogeneous fields. Real-time sensor systems for VRNA usually consider only the crop’s N status and their fertilization algorithms are abundantly deterministic. Due to their education and professional experience, farmers have a considerable knowledge base that should be used to describe the dynamic and non-deterministic interactions of multiple parameters for a locally adapted N fertilization. Fuzzy systems present an effective way to integrate expert knowledge into an automated multi-parametric control. This paper describes, how fuzzy logic can be used to fuse the plant-related information from a real-time sensor system with further parameters to create a multi-parametric system for VRNA. Using sets of input–output data acquired with a Yara N-Sensor ALS2 system, an adaptive, fuzzy logic-based model of its agronomic algorithms was identified, optimized and validated. The results indicated high accordance with the N-Sensor algorithms and good automated adaptability to different calibrations with values of the Pearson correlation coefficient higher than 0.99 and a maximum percentage root mean square error of 0.14%. In a case study, the model was combined with the apparent soil electrical conductivity (ECa) as an indicator for spatially varying soil productivity, as well as a case distinction for different weather conditions. Simulations with historic ECa data and N-Sensor recordings have shown the high flexibility of the multi-parametric fuzzy expert system. With the presented method, specific deficiencies of one-parametric approaches can be moderated and the application can be adapted to the prevailing conditions in a straightforward manner. Also, the target orientation could be influenced based on the specific preferences of the expert.

传统作物种植中由于矿物施肥而导致的氮（N）过量对环境产生重大负面影响。可变速率氮肥应用（VRNA）是提高空间异质田中氮素回收率的有前途的工具。用于VRNA的实时传感器系统通常仅考虑作物的N状态，其施肥算法具有很强的确定性。由于他们的教育和专业经验，农民拥有相当多的知识基础，这些知识应用于描述针对局部适应氮肥的多个参数的动态和不确定性相互作用。模糊系统提供了一种将专家知识集成到自动化多参数控制中的有效方法。本文介绍了 如何使用模糊逻辑将实时传感器系统中与植物相关的信息与其他参数融合在一起，以创建VRNA的多参数系统。使用通过Yara N-Sensor ALS2系统获取的输入输出数据集，可以识别，优化和验证基于自适应模糊逻辑的农学算法模型。结果表明，与N传感器算法高度吻合，并且对不同校准具有良好的自动化适应性，皮尔森相关系数的值高于0.99，最大均方根误差为0.14％。在一个案例研究中，该模型与表观土壤电导率（ECa）相结合，作为土壤生产力在空间上变化的指标以及不同天气条件下的案例区分。使用历史ECa数据和N-Sensor记录进行的仿真表明，多参数模糊专家系统具有高度的灵活性。使用所提出的方法，可以缓解单参数方法的特定缺陷，并且可以以直接的方式使应用程序适应主要条件。而且，可以根据专家的特定偏好来影响目标方向。