| 基于可见光图像和卷积神经网络的冬小麦苗期长势参数估算 |
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| 引用本文： | 马浚诚,刘红杰,郑飞翔,杜克明,张领先,胡新,孙忠富.基于可见光图像和卷积神经网络的冬小麦苗期长势参数估算[J].农业工程学报,2019,35(5):183-189. |
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| 作者姓名： | 马浚诚 刘红杰 郑飞翔 杜克明 张领先 胡新 孙忠富 |
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| 作者单位： | 1. 中国农业科学院农业环境与可持续发展研究所，北京 100081；,2. 河南省商丘市农林科学院小麦研究所，商丘 476000；,1. 中国农业科学院农业环境与可持续发展研究所，北京 100081；,1. 中国农业科学院农业环境与可持续发展研究所，北京 100081；,3. 中国农业大学信息与电气工程学院，北京 100083,2. 河南省商丘市农林科学院小麦研究所，商丘 476000；,1. 中国农业科学院农业环境与可持续发展研究所，北京 100081； |
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| 基金项目： | 国家自然科学基金（31801264）；国家重点研发计划项目（2016YFD0300606和2017YFD0300402） |
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| 摘 要： | 针对目前基于计算机视觉估算冬小麦苗期长势参数存在易受噪声干扰且对人工特征依赖性较强的问题,该文综合运用图像处理和深度学习技术,提出一种基于卷积神经网络(convolutional neural network, CNN)的冬小麦苗期长势参数估算方法。以冬小麦苗期冠层可见光图像作为输入,构建了适用于冬小麦苗期长势参数估算卷积神经网络模型,通过学习的方式建立冬小麦冠层可见光图像与长势参数的关系,实现了农田尺度冬小麦苗期冠层叶面积指数(leaf area index,LAI)和地上生物量(above ground biomass, AGB)的准确估算。为验证方法的有效性,该研究采用以冠层覆盖率(canopy cover, CC)作为自变量的线性回归模型和以图像特征为输入的随机森林(random forest, RF)、支持向量机回归(support vectormachinesregression,SVM)进行对比分析,采用决定系数(coefficientofdetermination,R2)和归一化均方根误差(normalized root mean square error, NRMSE)定量评价估算方法的准确率。结果表明:该方法估算准确率均优于对比方法,其中AGB估算结果的R2为0.7917,NRMSE为24.37%,LAI估算结果的R2为0.8256,NRMSE为23.33%。研究可为冬小麦苗期长势监测与田间精细管理提供参考。
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| 关 键 词： | 作物 生长 参数估算 冬小麦 苗期 叶面积指数 地上生物量 卷积神经网络 |
| 收稿时间： | 2018/9/27 0:00:00 |
| 修稿时间： | 2019/1/17 0:00:00 |
Estimating growth related traits of winter wheat at seedling stages based on RGB images and convolutional neural network |
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| Ma Juncheng,Liu Hongjie,Zheng Feixiang,Du Keming,Zhang Lingxian,Hu Xin and Sun Zhongfu.Estimating growth related traits of winter wheat at seedling stages based on RGB images and convolutional neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2019,35(5):183-189. |
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| Authors: | Ma Juncheng Liu Hongjie Zheng Feixiang Du Keming Zhang Lingxian Hu Xin and Sun Zhongfu |
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| Institution: | 1. Institute of Environment and sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China;,2. Wheat Research Laboratory, Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, China;,1. Institute of Environment and sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China;,1. Institute of Environment and sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China;,3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China,2. Wheat Research Laboratory, Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, China; and 1. Institute of Environment and sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China; |
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| Abstract: | Abstract: Leaf area index (LAI) and above ground biomass (AGB) are two critical traits indicating the growth of winter wheat. Currently, non-destructive methods for measuring LAI and AGB heavily are subjected to limitations that the methods are susceptible to the environmental noises and greatly depend on the manual designed features. In this study, an easy-to-use growth-related traits estimation method for winter wheat at early growth stages was proposed by using digital images captured under field conditions and Convolutional Neural Network (CNN). RGB images of winter wheat canopy in 12 plots were captured at the field station of Shangqiu Academy of Agriculture and Forestry Sciences, Henan, China. The canopy images were captured by a low-cost camera at the early growth stages. Using canopy images at early growth stages as input, a CNN structure suitable for the estimation of growth related traits was explored, which was then trained to learn the relationship between the canopy images and the corresponding growth-related traits. Based on the trained CNN, the estimation of LAI and AGB of winter wheat at early growth stages was achieved. In order to compare the results of the CNN, conventionally adopted methods for estimating LAI and AGB in conjunction with a collection of color and texture feature extraction techniques were used. The conventional methods included a linear regression model using canopy cover as the predictor variable (LR-CC), Random Forest (RF) and Support Vector Machine Regression (SVR). The canopy images of winter wheat were captured at early growth stages, resulting in the existence of pixels representing non-vegetation elements in these images, such as soil. Therefore, it was necessary to perform image segmentation of vegetation for the compared methods prior to feature extraction. The segmentation was achieved by Canopeo. The linear regression was used to compare the accuracy of the methods. Normalized Root-Mean-Squared error (NRMSE) and coefficient of determination (R2) were used as the criterion for model evaluation. Results showed the CNN demonstrated superior results to the compared methods in the two metrics. Strong correlations could be observed between the actual measurements of traits to those estimated by the CNN. The estimation results of LAI had R2 equaled to 0.825 6 and NRMSE equaled to 23.33%, and the results of AGB had R2 equaled to 0.791 7 and NRMSE equals to 24.37%. Compare to the comparative methods, the CNN was a more direct method for AGB and LAI estimation. The image segmentation of vegetation was not necessary because the CNN was able to use the important features to estimate AGB and LAI and ignore the non-important features, which not only reduced the computation cost but also increased the efficiency of the estimation. In contrast, the performances of the compared estimating methods greatly depended on the results of image segmentation. Accurate segmentation results guaranteed accurate data sources to feature extraction. However, canopy images captured under real field conditions were suffering from uneven illumination and complicated background, which was a big challenge to achieve robust image segmentation of vegetation. It was revealed that robust estimation of AGB and LAI of winter wheat at early growth stages could be achieved by CNN, which can provide support to growth monitoring and field management of winter wheat. |
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| Keywords: | crops growth parameter estimation winter wheat seedling stages leaf area index above ground biomass convolutional neural network |
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