Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Jianqing Zhao; Yucheng Cai; Suwan Wang; Jiawei Yan; Xiaolei Qiu; Xia Yao; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaohu Zhang

doi:10.34133/plantphenomics.0109

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Plant Phenomics. 2023 Oct 30:5:0109. doi: 10.34133/plantphenomics.0109. eCollection 2023.

Authors

Jianqing Zhao^{1

2}, Yucheng Cai^{1

2}, Suwan Wang^{1

2}, Jiawei Yan^{1

2}, Xiaolei Qiu^{1

2}, Xia Yao^{1

2

3}, Yongchao Tian^{1

4}, Yan Zhu^{1

2}, Weixing Cao^{1

2}, Xiaohu Zhang^{1

2

4}

Affiliations

¹ National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
² Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China.
³ Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095, China.
⁴ Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, China.

Abstract

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.