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

• 专刊--农业信息感知与模型 • 上一篇    下一篇

用于小麦多生长阶段倒伏边界精准检测的分层交互特征金字塔网络

庞春晖1,6,7, 陈鹏1,6,7(), 夏懿1, 章军1, 王兵2, 邹岩3,4, 陈天娇3,4, 康辰瑞3,5, 梁栋1()   

  1. 1. 安徽大学农业生态大数据分析与应用技术国家地方联合工程研究中心/信息材料与智能感知安徽省实验室/安徽大学互联网学院，安徽 合肥 230601，中国
    2. 安徽财经大学 管理科学与工程学院，安徽 蚌埠 233030，中国
    3. 中国科学院合肥物质科学院智能机械研究所，安徽 合肥 230031，中国
    4. 中国科学技术大学，安徽 合肥 230031，中国
    5. 西南科技大学，四川 绵阳 621010，中国
    6. 农业传感器与智能感知安徽省技术创新中心，中科合肥智慧农业协同创新研究院，安徽 合肥 231131，中国
    7. 安徽鹏视智能科技有限公司，安徽 合肥 230000，中国
  • 收稿日期:2023-10-03 出版日期:2024-03-30
  • 基金资助:
    国家自然科学基金项目(62072002;62273001); 安徽省科技重大专项(202003a06020016); 安徽省现代农业产业技术体系建设专项资金（2021—2025）; 安徽省高校优秀科研创新团队(2022AH010005)
  • 作者简介:

    庞春晖， E-mail：

  • 通信作者:
    1. 陈 鹏， E-mail： ；
    2. 梁 栋， E-mail：

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)

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摘要：

[目的/意义] 传统的小麦倒伏检测方法需要人工进行田间观测和记录，这种方法存在主观、效率低、劳动强度大等问题，难以满足大规模的小麦倒伏检测的需求。基于深度学习的小麦倒伏检测技术虽已在一定程度上得到应用，但普遍局限于对小麦单一发育阶段的倒伏识别，而倒伏可能发生在小麦生长的各个时期，不同时期倒伏特征变化复杂，这给模型特征捕捉能力带来考验。本研究旨在探索一种基于深度学习技术的多生育期小麦倒伏区域检测方法。 [方法] 用无人机采集小麦灌浆期、早熟期、晚熟期这三个关键生长阶段的RGB图像，通过数据增强等技术构建出多生育期小麦倒伏数据集。提出一种小麦倒伏提取模型Lodging2Former，该模型在Mask2Former的基础上加以改进，引入分层交互式特征金字塔网络（Hierarchical Interactive Feature Pyramid Network, HI-FPN ），用于提高模型在复杂田间背景干扰下对于多个生长阶段小麦倒伏特征的捕捉能力。 [结果和讨论] 所提出的Lodging2Former模型相较于现存的多种主流算法，如Mask R-CNN （Mask Region-Based Convolutional Neural Network）、SOLOv2（Segmenting Objects by Locations, Version 2）以及Mask2Former，在平均精度均值（mean Average Precision, mAP）上展现出显著优势。在阈值分别为0.5、0.75以及0.5~0.95的条件下，模型的mAP值分别达到了79.5%、40.2%和43.4%，相比Mask2Former模型，mAP性能提升了1.3%~4.3%。 [结论] 提出的HI-FPN网络可以有效利用图像中的上下文语义和细节信息，通过提取丰富的多尺度特征，增强了模型对小麦在不同生长阶段倒伏区域的检测能力，证实了HI-FPN在多生育期小麦倒伏检测中的应用潜力和价值。

关键词: 无人机, 深度学习, 小麦倒伏检测, 特征金字塔网络, Mask2Former

Abstract:

[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

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