Abstract
ABSTRACT Ear count serves as a crucial parameter for estimating wheat yield, holding great significance in crop phenotypic parameter calculations, yield predictions and wheat ear density management. To accurately determine the number of wheat ears, this study utilized the Global Wheat Head Detection 2021 dataset, and introduced a wheat ear counting method based on the improved YOLOX model. An attention mechanism was integrated into the feature extraction network to bolster the semantic information of the feature map. This enhancement aimed to improve the capability of effectively detecting wheat ears. By using GIoU loss to optimize the loss function, the positioning accuracy of bounding box was improved. Experimental results show that, compared with other models, the improved YOLOX model has significantly improved the detection effect of different targets. Among them, the precision of the improved YOLOX model is 97.24%, the average precision is 94.15%, the accuracy is 99.76%, and the log average miss detection rate is 0.25. This study provides a reliable method for precision wheat ear identification, offering valuable applications in high-throughput investigation of wheat ear traits.
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