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Abstract. In several studies, deep learning algorithms demonstrated feasibility for flower detection and recognition in images. YOLOX is a new high-performance object detector that is the latest in the YOLO (You Only Look Once) deep learning network series that trains on full images and directly optimizes detection performance. No study has been done on peanut flower detection before. This study developed deep learning algorithms using the latest YOLOX models to detect peanut flowers (Arachis hypogaea) from images collected from a peanut research station. YOLOX-L and YOLOX-X models were compared with different configurations to find the best YOLOX object detector for peanut flower detection. Built-in Mosaic and MixUp data augmentation strategies were applied to the training and validation datasets. For experimental purposes, the data augmentation was reduced or enhanced. Another experiment was when the data augmentation strategy, MixUp, was turned off or on. The mean average precision (mAP) evaluation metric was used to evaluate the performance of the YOLOX models. The original 300 epoch recommended by YOLOX developers was tested on the training/validation dataset and was reduced to 100 based on several preliminary tests. YOLOX-X achieved 90.37% mAP with weak/reduced data augmentation on the testing dataset. The average inference time for the unseen images in the testing dataset was 0.134 seconds. YOLOX-X demonstrated feasibility for detecting peanut flowers from in-field acquired images. The presented method will assist researchers in developing a counting method on peanut flowers in images and implementing the detection technique with required minor modifications for other crops or flowers.