Accurate parcel geometry is important for many applications in agriculture and forestry. The assessments of agricultural parcel geometries are, e.g., crucial for calculating financial subsidies. Today, authorities use aerial images to check parcels for homogeneous cultivation and incorrect parcel geometries (homogeneity-check). Due to the large number of parcels and limited resources of authority offices, there is a need for an automated method of risk-based selection of a manageable subset of parcels for manual review. To this end, this study evaluated four machine learning approaches; Image Classification, Semantic Segmentation, Edge Detection, and Clustering, with respect to (i) their sensitivity to spatial resolution of aerial images, (ii) the impact of the balancing method used in training, and (iii) their overall performance with respect to preselecting parcels for homogeneity-check. In addition, we introduced a modified version of the commonly used Precision@K metric. The proposed Precision@K% replaces the absolute number of top-ranked items with a relative proportion, facilitating a more scalable evaluation of risk-based selection strategies. The results show that all approaches performed similarly in terms of the area under the receiver operating characteristic curve (AUROC) and the Precision@2% metric; Image Classification: AUROC = 77.4%, Precision@2% = 80%, Semantic Segmentation: AUROC = 83.0%, Precision@2% = 75%, Edge Detection: AUROC = 79.2%, Precision@2% = 85%, and Clustering: AUROC = 84.4%, Precision@2% = 100%. Notably, the Clustering approach consistently maintained a stable Precision@2% of 100%.