| 基于RGB图像与深度学习的冬小麦田间长势参数估算系统 |
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| 引用本文： | 李云霞,马浚诚,刘红杰,张领先.基于RGB图像与深度学习的冬小麦田间长势参数估算系统[J].农业工程学报,2021,37(24):189-198. |
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| 作者姓名： | 李云霞 马浚诚 刘红杰 张领先 |
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| 作者单位： | 1. 中国农业大学信息与电气工程学院，北京 100083;2. 中国农业科学院农业环境与可持续发展研究所，北京 100081;3. 河南省商丘市农林科学院小麦研究所，商丘 47600 |
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| 基金项目： | 国家自然科学基金（31801264）；中国科协青年人才托举工程第四届项目（2018QNRC001） |
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| 摘 要： | 为准确、快速获取冬小麦田间长势信息，该研究设计并实现了一种基于深度学习的冬小麦田间长势参数估算系统。该系统主要包含长势参数估算模块和麦穗计数模块。长势参数估算模块基于残差网络ResNet18构建长势参数估算模型，实现了冬小麦苗期叶面积指数（Leaf Area Index，LAI）和地上生物量（Above Ground Biomass，AGB）的估算，并基于迁移学习进行泛化能力测试；麦穗计数模块基于Faster R-CNN并结合非极大值抑制（Non Maximum Suppression，NMS）构建麦穗计数模型，实现了开花期麦穗准确计数。结果表明，针对2017-2018和2018-2019两个生长季数据，基于ResNet18的长势参数估算模型对LAI估算的决定系数分别为0.83和0.80，对AGB估算的决定系数均为0.84，优于基于传统卷积神经网络（Convolutional Neural Networks，CNN）、VGG16和GoogLeNet构建的估算模型，并且泛化能力测试表明该模型对数据的季节性差异鲁棒。基于Faster R-CNN的麦穗计数模型，在利用NMS优化后决定系数从0.66增至0.83，提升了25.8%，NRMSE从0.19降至0.05，下降了73.7%。相较于基于CNN构建的分类计数模型，基于Faster R-CNN+NMS的麦穗计数模型表现更优，决定系数为0.83，提升了33.87%，单个麦穗识别时间为1.009 s，效率提升了20.55%。综上所述，该系统能够满足冬小麦田间长势参数估算需求，为冬小麦田间精细化管理提供支撑。
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| 关 键 词： | 机器视觉 图像处理 模型 冬小麦 深度学习 叶面积指数 地上生物量 麦穗计数 |
| 收稿时间： | 2021/1/21 0:00:00 |
| 修稿时间： | 2021/12/11 0:00:00 |
Field growth parameter estimation system of winter wheat using RGB digital images and deep learning |
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| Li Yunxi,Ma Juncheng,Liu Hongjie,Zhang Lingxian.Field growth parameter estimation system of winter wheat using RGB digital images and deep learning[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(24):189-198. |
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| Authors: | Li Yunxi Ma Juncheng Liu Hongjie Zhang Lingxian |
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| Institution: | 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;;2. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China;;3. Wheat Research Laboratory, Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, China |
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| Abstract: | Abstract: Leaf Area Index (LAI) and Above Ground Biomass (AGB) are the key traits to fully reflect the growth of winter wheat at early stages. After winter wheat enters the flowering stage, the development of wheat ears has been basically completed. The number of ears is a vital agronomic parameter to characterize the growth of winter wheat at this stage, and also a critical factor to evaluate the germplasm and yield of winter wheat. At present, the traditional methods for measuring LAI, AGB and ear number are destructive and time-consuming, and the accuracy of estimation methods based on RGB images and shallow machine learning technology needs to be further improved. In order to accurately and quickly obtain the winter wheat growth information, and further improve the accuracy of winter wheat growth parameter estimation, this study developed a winter wheat growth parameter estimation system based on deep learning. The system mainly included growth parameter estimation module and wheat ear counting module. The data of winter wheat during the 2017-2018 and 2018-2019 growing seasons were collected consecutively. Combined with the characteristics of RGB images, the deep learning models was explored, which were applicable to obtain the growth parameters at the early stage of winter wheat and to count the number of wheat ears. Therefore, for the estimation module of growth parameter at early stages, the residual network ResNet18 was used as the basic network to establish the growth parameter estimation model, with the 2017-2018 growth season data. Based on this estimation model, the LAI and AGB at early stages of winter wheat were obtained. Moreover, the generalization ability of the ResNet18-based model was tested using transfer learning, with 2018-2019 growth season data. For wheat ear counting module, a wheat ear counting model was built, which was based on the Faster R-CNN and Non Maximum Suppression (NMS), and achieved accurate counting of wheat ear at flowering stage. Moreover, the Faster R-CNN+NMS wheat ear counting model was compared with the Faster R-CNN ear counting model without NMS and the classification counting model based on Convolutional Neural Networks (CNNs). The results showed that, for the estimation module of growth parameter at early stages, the determination coefficients of the ResNet18-based model for LAI estimation respectively were 0.83 and 0.80 on the dataset of the two growing seasons of 2017-2018 and 2018-2019. And the determination coefficients of the ResNet18-based model for AGB estimation both were 0.84. The model was superior to the model based on VGG16 and GoogLeNet and the published CNN-based estimation model. And the results of generalization ability test showed that the ResNet18-based model was robust to the seasonal differences of data. For wheat ear counting module, given the ear counting model based on Faster R-CNN, the determination coefficient increased by 25.8% from 0.66 to 0.83, after the NMS optimization. And the NRMSE decreased by 73.7% from 0.19 to 0.05. Compared with the classification counting model based on CNN, the wheat ear counting model based on Faster R-CNN+NMS had better performance, with a determination coefficient of 0.83, improved by 33.87%, and a single ear identified time of 1.009 s, improved by 20.55%. In conclusion, this system can meet the demand of field growth parameter estimation of winter wheat and provide support for fine field management of winter wheat. |
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| Keywords: | machine vision image processing models winter wheat deep learning leaf area index above ground biomass wheat ear counting |
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