Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

中文翻译：

---

用空间数据融合和信念理论划分管理区域

精准农业作为现代农业的一部分，依靠大量增长的信息和数据进行处理和应用。用于产量预测或管理区划定的空间数据非常多样化，通常质量不同，单位也不同。由于各种原因，组合地理数据的方法很复杂，但如果要考虑所有相关信息，则是必要的。与信念结构的数据融合提供了将地理数据与专家知识联系起来的可能性，在过程中包含经验和信念，并与其他“黑匣子”模型相比，保持框架的可理解性。本研究显示了通过结合土壤信息将农地划分为管理区的可能性，使用可转移置信模型的浮雕结构和多时相卫星数据。它能够将农民的知识和经验引入他们的田地，因此可以在管理措施方面提供实际帮助，而无需做出失控的决定。同时，该方法提供了一种将与作物活力和产量相关的所有有价值的空间数据结合起来的解决方案。为了开发该方法，融合了每种可能组合和不同模型参数中的 11 个数据集。本研究介绍了与实践最相关的结果和模型的可理解性。该方法的目标是划分为三个等级的田间地图：“低产量”、“中等产量”和“高产量”。结果表明，并非所有数据都与产量等级建模同样相关，并且植物物候学对于卫星图像的选择尤为重要。结果得到了产量数据的验证，并显示出在精准农业中的应用潜力。