Welcome to Smart Agriculture

Smart Agriculture ›› 2024, Vol. 6 ›› Issue (2): 128-139.doi: 10.12133/j.smartag.SA202310002

• Special Issue--Agricultural Information Perception and Models • Previous Articles     Next Articles

HI-FPN: A Hierarchical Interactive Feature Pyramid Network for Accurate Wheat Lodging Localization Across Multiple Growth Periods

PANG Chunhui1,6,7, CHEN Peng1,6,7(), XIA Yi1, ZHANG Jun1, WANG Bing2, ZOU Yan3,4, CHEN Tianjiao3,4, KANG Chenrui3,5, LIANG Dong1()   

  1. 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application/ Information Materials and Intelligent Sensing Laboratory of Anhui Province/ Institutes of Physical Science and Information Technology & School of Internet, Anhui University, Hefei 230601, China
    2. School of Management Science and Engineering, Anhui University of Finance & Economics, Bengbu 233030, China
    3. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Science, Hefei 230031, China
    4. University of Science and Technology of China, Hefei 230031, China
    5. Southwest University of Science and Technology, Mianyang 621010, China
    6. Agricultural Sensors and Intelligent Perception Technology Innovation Center of Anhui Province, Zhongke Hefei Institutes of Collaborative Research and Innovation for Intelligent Agriculture, Hefei 231131, China
    7. Anhui Rocvision Intelligent Technology Co. , Ltd, Hefei 230000, China
  • Received:2023-10-03 Online:2024-03-30
  • corresponding author:
    1. CHEN Peng, Ph.D., Professor, research interests are computer vision and data analysis. E-mail: ;
    2. LIANG Dong, Ph.D., Professor, research interests are computer vision and smart agriculture. E-mail:
  • About author:

    PANG Chunhui, research interests is computer vision. E-mail:

  • Supported by:
    National Natural Science Foundation of China Projects(62072002;62273001); Anhui Provincial Major Science and Technology Special Project(202003a06020016); Supported by the Special Fund for Anhui Agriculture Research System (2021-2025); Excellent Scientific Research Innovation Team of Anhui Province Universities(2022AH010005)


[Objective] Wheat lodging is one of the key isuess threatening stable and high yields. Lodging detection technology based on deep learning generally limited to identifying lodging at a single growth stage of wheat, while lodging may occur at various stages of the growth cycle. Moreover, the morphological characteristics of lodging vary significantly as the growth period progresses, posing a challenge to the feature capturing ability of deep learning models. The aim is exploring a deep learning-based method for detecting wheat lodging boundaries across multiple growth stages to achieve automatic and accurate monitoring of wheat lodging. [Methods] A model called Lodging2Former was proposed, which integrates the innovative hierarchical interactive feature pyramid network (HI-FPN ) on top of the advanced segmentation model Mask2Former. The key focus of this network design lies in enhancing the fusion and interaction between feature maps at adjacent hierarchical levels, enabling the model to effectively integrate feature information at different scales. Building upon this, even in complex field backgrounds, the Lodging2Former model significantly enhances the recognition and capturing capabilities of wheat lodging features at multiple growth stages. [Results and Discussions] The Lodging2Former model demonstrated superiority in mean average precision (mAP) compared to several mainstream algorithms such as mask region-based convolutional neural network (Mask R-CNN), segmenting objects by locations (SOLOv2), and Mask2Former. When applied to the scenario of detecting lodging in mixed growth stage wheat, the model achieved mAP values of 79.5%, 40.2%, and 43.4% at thresholds of 0.5, 0.75, and 0.5 to 0.95, respectively. Compared to Mask2Former, the performance of the improved model was enhanced by 1.3% to 4.3%. Compared to SOLOv2, a growth of 9.9% to 30.7% in mAP was achieved; and compared to the classic Mask R-CNN, a significant improvement of 24.2% to 26.4% was obtained. Furthermore, regardless of the IoU threshold standard, the Lodging2Former exhibited the best detection performance, demonstrating good robustness and adaptability in the face of potential influencing factors such as field environment changes. [Conclusions] The experimental results indicated that the proposed HI-FPN network could effectively utilize contextual semantics and detailed information in images. By extracting rich multi-scale features, it enabled the Lodging2Former model to more accurately detect lodging areas of wheat across different growth stages, confirming the potential and value of HI-FPN in detecting lodging in multi-growth-stage wheat.

Key words: drone, deep learning, wheat lodging detection, feature pyramid network, Mask2Former

CLC Number: