Early disease detection is required, considering the impacts of diseases on crop yield. However, current methods involve labor-intensive data collection. Thus, unsupervised anomaly detection in time series imagery was proposed, requiring high-resolution unmanned aerial vehicle (UAV) imagery and sophisticated algorithms to identify unknown anomalies amidst complex data patterns to cope with within season crop monitoring and background challenges. The dataset used in this study was acquired by a Micasense Altum sensor on a DJI Matrice 210 UAV with a 4 mm resolution in Gottingen, Germany. The proposed methodology includes (1) date selection for finding the date sensitive to sugar beet changes, (2) vegetation index (VI) selection for finding the one sensitive to sugar beet and its temporal patterns by visual inspection, (3) sugar beet extraction using thresholding and morphological operator, and (4) an ensemble of bottom-up, Kernel, and quadratic discriminate analysis methods for unsupervised time series anomaly detection. The study highlighted the importance of the wide-dynamic-range VI and morphological filtering with time series trimming for accurate disease detection while reducing background errors, achieving a kappa of 76.57%, comparable to deep learning model accuracies, indicating the potential of this approach. Also, 81 days after sowing, image acquisition could begin for cost and time efficient disease detection.