Individual tree crown detection and delineation (ITCD) algorithms extract the boundaries of individual tree crowns from images. State-of-the-art machine learning techniques segment crowns efficiently and cost-effectively using drones and multispectral sensors. Farmers use this emerging technology to help improve crop yields, reduce resource inputs, and improve management approaches, ultimately enhancing agricultural sustainability. However, ITCD methods often fail to delineate all tree boundaries accurately. Poorly estimated delineations significantly limit the efficiency of monitoring and management strategies within orchards. This work proposes a new processing pipeline to detect three commonly occurring anomalous delineations: i) under-segmentation, ii) over-segmentation, and iii) false positives. A hand-crafted feature set comprising coarse shape descriptors, Haralick features, and spectral indices are extracted from orchard imagery to accomplish this. Furthermore, a novel approach was developed based on Geary’s c statistic in a multivariate context, leveraging physical neighbourhoods to identify contextual outlierness. This method was evaluated – using average precision and AUC-ROC performance measures – against unsupervised anomaly detection algorithms, including isolation forest (IForest), angle-based outlier detection (ABOD), and PCA-based anomaly detection. The study also trained a meta-learner to automate anomaly detection in new orchards. Results show that local outlier detection methods effectively capture over-segmentation. In contrast, global outlier detection methods better capture under-segmentation and false positives and fare well in general cases where multiple anomaly types are present. Overall, ABOD, IForest and PCA are the top models for detecting poor delineations (average AUC scores: 0.97, 0.964, and 0.962, respectively). The Geary-based approach (GBOD) gave less consistent results (average AUC score: 0.943). Nevertheless, it identified problematic regions while attributing outlierness, a capability none of the top three possess. Finally, the paper successfully demonstrates automatic model selection and anomaly detection for out-of-sample delineations, a problematic task due to the lack of reference data.