Bridging the gap between hyperspectral imaging and crop breeding: soybean yield prediction and lodging classification with prototype contrastive learning

Guangyao Sun, Yong Zhang, Lei Wang, Longyu Zhou, Shuaipeng Fei, Shiteng Han, Shunfu Xiao, Yingpu Che, Long Yan, Yun Xu, Yinghui Li, Lijuan Qiu, Yuntao Ma

Research output: Contribution to journalArticlepeer-review

Abstract

Yield and lodging are crucial indicators in soybean breeding. The development of unmanned aerial vehicle (UAV) equipped with hyperspectral imaging technologies provides high-throughput data for estimating these factors. Previous studies have primarily focused on using hand-crafted band reflectance information, vegetation indices, and texture features to construct empirical models for yield and lodging estimation. However, few studies have directly employed deep learning techniques to automatically extract features from raw hyperspectral images in this context. The objectives were to investigate the potential of combining hyperspectral images with deep learning for soybean yield and lodging prediction, and to avoid the complex process of traditional feature extraction. A novel Prototype Contrastive Learning (PCL) network was proposed to learn representations from raw images. For comparison, hand-crafted vegetation indices and texture features, selected for their effectiveness in crop growth monitoring, were extracted and input into the same machine learning model. The impact of different growth stages on yield and lodging prediction was then investigated. Results demonstrated that the PCL network can effectively capture the similarities within the same class and the differences between different classes. The PCL representations exhibited more distinct clusters according to class labels compared to hand-crafted features. At 86 days after emergence (DAE), the PCL method achieved optimal yield prediction accuracy (R2 = 0.65, RMSE = 507.56 kg/ha) and was significantly higher than the hand-crafted features method (R2 = 0.55, RMSE = 581.37 kg/ha). The highest performance of lodging grades classification was achieved at 65 DAE, and the PCL representations (F1-score = 0.80) achieved a 48 % accuracy improvement compared to hand-crafted features (F1-score = 0.54). This study pioneered the use of deep learning for automatic hyperspectral feature extraction in real-world breeding scenarios, providing valuable insights and strategies to improve yield prediction and lodging classification, thereby more effectively supporting soybean breeding and field management.

Original languageEnglish
Article number109859
JournalComputers and Electronics in Agriculture
Volume230
DOIs
StatePublished - Mar 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025 Elsevier B.V.

Keywords

  • Deep Learning
  • Resnet
  • Soybean Yield and Lodging Prediction
  • Unmanned Aerial Vehicle

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

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