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Generalized Focal Loss WheatNet (GFLWheatNet): Accurate Application of a Wheat Ear Detection Model in Field Yield Prediction

文献类型： 外文期刊

作者： Guan, Yujie 1 ; Pan, Jiaqi 1 ; Fan, Qingqi 2 ; Yang, Liangliang 3 ; Xu, Li 4 ; Jia, Weikuan 1 ;

作者机构： 1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China

2.Shandong Acad Agr Sci, Crop Res Inst, Jinan 250100, Peoples R China

3.Kitami Inst Technol, Fac Engn, Kitami 0908507, Japan

4.Zaozhuang Univ, Sch Informat Sci & Engn, Zaozhuang 277160, Peoples R China

5.Key Lab Facil Agr Measurement & Control Technol &, Zhenjiang 212013, Peoples R China

关键词： deep learning; object detection; wheat ear; GWHD-2021; dense detection

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.3； 五年影响因子：3.5 ）

ISSN：

年卷期： 2024 年 14 卷 6 期

页码：

收录情况： SCI

摘要： Wheat ear counting is crucial for calculating wheat phenotypic parameters and scientifically managing fields, which is essential for estimating wheat field yield. In wheat fields, detecting wheat ears can be challenging due to factors such as changes in illumination, wheat ear growth posture, and the appearance color of wheat ears. To improve the accuracy and efficiency of wheat ear detection and meet the demands of intelligent yield estimation, this study proposes an efficient model, Generalized Focal Loss WheatNet (GFLWheatNet), for wheat ear detection. This model precisely counts small, dense, and overlapping wheat ears. Firstly, in the feature extraction stage, we discarded the C4 feature layer of the ResNet50 and added the Convolutional block attention module (CBAM) to this location. This step maintains strong feature extraction capabilities while reducing redundant feature information. Secondly, in the reinforcement layer, we designed a skip connection module to replace the multi-scale feature fusion network, expanding the receptive field to adapt to various scales of wheat ears. Thirdly, leveraging the concept of distribution-guided localization, we constructed a detection head network to address the challenge of low accuracy in detecting dense and overlapping targets. Validation on the publicly available Global Wheat Head Detection dataset (GWHD-2021) demonstrates that GFLWheatNet achieves detection accuracies of 43.3% and 93.7% in terms of mean Average Precision (mAP) and AP50 (Intersection over Union (IOU) = 0.5), respectively. Compared to other models, it exhibits strong performance in terms of detection accuracy and efficiency. This model can serve as a reference for intelligent wheat ear counting during wheat yield estimation and provide theoretical insights for the detection of ears in other grain crops.

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