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

Yujie Guan, Jiaqi Pan, Qingqi Fan, Liangliang Yang, Li Xu () and Weikuan Jia ()
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Yujie Guan: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Jiaqi Pan: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Qingqi Fan: Crop Research Institute, Shandong Academy of Agricultural Science, Jinan 250100, China
Liangliang Yang: Faculty of Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Li Xu: School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China
Weikuan Jia: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Agriculture, 2024, vol. 14, issue 6, 1-20

Abstract: 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 A P 50 (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.

Keywords: deep learning; object detection; wheat ear; GWHD-2021; dense detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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