基于图像特征的水稻叶片全氮含量估测模型研究

doi:10.3969/j.issn.1004-1524.2020.12.15

浙江农业学报 ›› 2020, Vol. 32 ›› Issue (12): 2232-2243.DOI: 10.3969/j.issn.1004-1524.2020.12.15

基于图像特征的水稻叶片全氮含量估测模型研究

杨红云¹(), 罗建军², 孙爱珍², 万颖², 易文龙¹

1.江西农业大学软件学院/江西省高等学校农业信息技术重点实验室,江西南昌330045
2.江西农业大学计算机与信息工程学院,江西南昌330045

收稿日期:2020-04-17 出版日期:2020-12-25 发布日期:2020-12-25
作者简介:杨红云(1975—),男,江西新干人,硕士,副教授,主要从事农业信息技术研究工作。E-mail: nc_yhy@163.com
基金资助:
国家自然科学基金(61562039);国家自然科学基金(61762048);国家自然科学基金(61862032)

Study on estimation model of total nitrogen content in rice leaves based on image characteristics

YANG Hongyun¹(), LUO Jianjun², SUN Aizhen², WAN Ying², YI Wenlong¹

1. School of Software, Jiangxi Agricultural University/Key Laboratory of Agricultural Information Technology, Jiangxi Higher Education, Nanchang 330045, China
2. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045,China

Received:2020-04-17 Online:2020-12-25 Published:2020-12-25

摘要/Abstract

摘要：

探究水稻叶片生长外部颜色、几何形态特征与其全含氮量之间的定量描述关系,可以快速且准确地诊断水稻氮素营养状况。研究筛选了全氮含量估测敏感叶位,并比较了基于多元线性回归和机器学习方法的水稻敏感叶位全氮含量估测模型,为构建高性能的氮素营养定量诊断模型提供思路和方法。水稻田间试验于2017—2018年在江西省南昌市成新农场进行,供试水稻品种为两优培九,设置4个施氮水平(施氮水平从低到高为0、210、300和390 kg·hm^-2)。在水稻幼穗分化期和齐穗期,分别扫描获取水稻顶部第一完全展开叶叶片(顶1叶)、顶部第二完全展开叶叶片(顶2叶)以及顶部第三完全展开叶叶片(顶3叶)图像,共4 800张图像。通过图像处理技术获取25项水稻扫描叶颜色和几何形态特征,采用多元线性回归进行全氮含量估测,筛选出两个时期的敏感叶位,并应用机器学习方法建立水稻敏感叶位的全氮含量估测模型。与人工测量相比,通过图像处理方法获取的水稻叶片长宽平均相对误差分别为0.328%、3.404%;幼穗分化期顶3叶和齐穗期顶2叶较其他同期叶位更为敏感,且以幼穗分化期顶3叶最为敏感;应用机器学习建立的水稻敏感叶位全氮含量估测模型略优于多元线性回归模型,且采用BP神经网络建模最佳,幼穗分化期顶3叶模型验证集的RMSE_v=0.089、MRE_v=0.034、 $R^{2}_{v}$=0.887,齐穗期顶2叶模型验证集的RMSE_v=0.132、MRE_v=0.046、$R^{2}_{v}$=0.820。幼穗分化期顶3叶和齐穗期顶2叶的叶片图像特征最具有代表性,进行全氮含量估测更具可行性,可作为水稻氮素营养诊断的有效叶位。

关键词: 水稻, 叶片全氮含量, 多元线性回归, 机器学习, 支持向量机, BP神经网络

Abstract:

In order to explore the quantitative description of the relationship between the external color, geometric shape characteristics of rice leaf growth and its total nitrogen content, it can quickly and accurately diagnose the nitrogen nutrition status of rice. In this study, the sensitive leaf positions for total nitrogen content estimation were screened out, and the models based on multiple linear regression and machine learning methods for total nitrogen content estimation of rice sensitive leaf positions were compared, which provided ideas and methods for establishing high-performance quantitative diagnosis model of nitrogen nutrition. The field experiment of rice was carried out in Chengxin farm, Nanchang City, Jiangxi Province from 2017 to 2018. The tested rice variety was LYP9. Four nitrogen application levels (0, 210, 300 and 390 kg·hm^-2 from low to high) were set. At the panicle differentiation stage and full heading stage of rice, 4 800 images of the first fully expanded leaf (1st leaf), the second fully expanded leaf (2nd leaf) and the third fully expanded leaf (3rd leaf) at the top of rice were obtained by scanning. Twenty-five scanning leaf color and geometric shape characteristics of rice were acquired by image processing technology, and the sensitive leaf positions in two periods were screened by multiple linear regression, and the total nitrogen content estimation model of rice sensitive leaf positions was established by machine learning method. The average relative errors of the length and width of rice leaves obtained by image processing method were 0.328% and 3.404% respectively, compared with the manual measurement; the 3rd leaf at the panicle differentiation stage and the 2nd leaf at the full heading stage were more sensitive than other leaf positions at the same period, and the 3rd leaf at the panicle differentiation stage was the most sensitive; the model of estimating the total nitrogen content in sensitive leaves of rice based on machine learning was slightly better than the multiple linear regression, and BP neural network was the best, RMSE_v=0.089, MRE_v=0.034,$R^{2}_{v}$=0.887 in the 3rd leaf at the panicle differentiation stage; RMSE_v=0.132, MRE_v=0.046,$R^{2}_{v}$=0.820 in the top 2nd leaf at the full heading stage. The 3rd leaf of the panicle differentiation stage and the 2nd leaf of the full heading stage were the most representative and more feasible to estimate the total nitrogen content, which can be used as an effective leaf position for nitrogen nutrition diagnosis of rice.

Key words: rice, leaf total nitrogen content, multiple linear regression, machine learning, support vector machine, BP neural network

中图分类号:

S126

杨红云, 罗建军, 孙爱珍, 万颖, 易文龙. 基于图像特征的水稻叶片全氮含量估测模型研究[J]. 浙江农业学报, 2020, 32(12): 2232-2243.

YANG Hongyun, LUO Jianjun, SUN Aizhen, WAN Ying, YI Wenlong. Study on estimation model of total nitrogen content in rice leaves based on image characteristics[J]. Acta Agriculturae Zhejiangensis, 2020, 32(12): 2232-2243.

图/表 12

图1 六种通道分离的对比图像

Fig.1 Comparison image of six channel separation

图2 水稻扫描图像二值化及去除杂质前后对比图像

Fig.2 Binary image of rice scanning image and image comparison before and after impurity removal

图3 黑色背景下的水稻图像

Fig.3 Rice image in black background

图4 水稻图像最小外接矩形图像

Fig.4 Minimum circumscribed rectangle image of rice image

图5 水稻叶片及参照物轮廓图像

Fig.5 Contour image of rice leaves and references

图6 BP神经网络模型拓扑结构图

Fig.6 Topological structure of BP neural network model

图7 水稻叶长叶宽误差分析图

Fig.7 Analysis table of rice leaf length and leaf width error

图8 水稻图像特征与叶片全氮含量的相关系数分布图图中特征号1~9代表叶片颜色特征,分别为叶片R、叶片G、叶片B、叶片NRI、叶片NGI、叶片NBI、叶片H、叶片S、叶片I;特征号10~18代表叶鞘颜色特征,分别为叶鞘R、叶鞘G、叶鞘B、叶鞘NRI、叶鞘NGI、叶鞘NBI、叶鞘H、叶鞘S、叶鞘I;特征号19~25代表几何形态特征特征,分别为叶片长度、叶片宽度、叶片周长、叶片面积、面积周长比、面积长度比、长宽比。图中红色值表示在0.010水平双侧显著相关;蓝色值表示在0.050水平双侧显著相关。

Fig.8 Correlation coefficient distribution of rice image characteristics and leaf total nitrogen content Feature numbers 1 to 9 in the figure represented leaf R, leaf G, leaf B, leaf NRI, leaf NGI, leaf NBI, leaf H, leaf S, leaf I, respectively. Feature numbers 10 to 18 represented leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath NRI, leaf sheath NGI, leaf sheath NBI, leaf sheath H, leaf sheath S, leaf sheath I, respectively. Feature numbers 19 to 25 represented leaf length, leaf width, leaf perimeter, leaf area, area perimeter ratio, area length ratio, length width ratio, respectively. The red values showed bilateral significant correlation at 0.010 level; the blue values showed bilateral significant correlation at 0.050 level.

表1 多元线性回归建模自变量选择及模型检验结果

Table 1 Multivariate linear regression modeling independent variable selection and model test results

时期及叶位 Period and leaf position	选择特征 Selected characteristics	多元线性回归模型 Multivariate regression model	$R c 2$	$R v 2$	MRE_v	RMSE_v
幼穗分化期顶三叶 3rd leaf of the Young panicle stage	叶片R、叶片G、叶片NRI、叶片H、叶片宽度 Leaf R, leaf G, leaf NRI, leaf H, leaf width	y=5.800-0.008x₁-0.025x₂- 4.008x₃+0.010x₄+0.128x₅	0.862	0.875	0.035	0.094
齐穗期顶二叶 2nd leaf of the full heading stage	叶片NRI、叶片NBI、叶片H、叶鞘NRI、叶鞘H Leaf NRI, leaf NBI, leaf H, leaf sheath NRI, leaf sheath H	y=-7.917-0.519x₁-1.341x₂+ 0.118x₃+1.804x₄-0.002x₅	0.708	0.714	0.058	0.164
齐穗期顶三叶 3rd leaf of the full heading stage	叶鞘B、叶鞘NGI、叶鞘NBI、叶鞘S、叶鞘I Leaf sheath B, leaf sheath NGI, leaf sheath NBI, Leaf sheath S, leaf sheath I	y=2.743-0.043x₁-0.011x₂+ 15.004x₃-1.175x₄-2.380x₅	0.692	0.676	0.145	0.245

表1 多元线性回归建模自变量选择及模型检验结果

Table 1 Multivariate linear regression modeling independent variable selection and model test results

时期及叶位 Period and leaf position	选择特征 Selected characteristics	多元线性回归模型 Multivariate regression model	$R c 2$	$R v 2$	MRE_v	RMSE_v
幼穗分化期顶三叶 3rd leaf of the Young panicle stage	叶片R、叶片G、叶片NRI、叶片H、叶片宽度 Leaf R, leaf G, leaf NRI, leaf H, leaf width	y=5.800-0.008x₁-0.025x₂- 4.008x₃+0.010x₄+0.128x₅	0.862	0.875	0.035	0.094
齐穗期顶二叶 2nd leaf of the full heading stage	叶片NRI、叶片NBI、叶片H、叶鞘NRI、叶鞘H Leaf NRI, leaf NBI, leaf H, leaf sheath NRI, leaf sheath H	y=-7.917-0.519x₁-1.341x₂+ 0.118x₃+1.804x₄-0.002x₅	0.708	0.714	0.058	0.164
齐穗期顶三叶 3rd leaf of the full heading stage	叶鞘B、叶鞘NGI、叶鞘NBI、叶鞘S、叶鞘I Leaf sheath B, leaf sheath NGI, leaf sheath NBI, Leaf sheath S, leaf sheath I	y=2.743-0.043x₁-0.011x₂+ 15.004x₃-1.175x₄-2.380x₅	0.692	0.676	0.145	0.245

表2 水稻敏感叶位叶片全氮含量估测模型验证结果对比

Table 2 Comparison of validation results of estimation model of total nitrogen content in sensitive leaves of rice

模型 Model	幼穗分化期顶三叶 3rd leaf at the young panicle stage			齐穗期顶二叶 2nd leaf at the full heading stage
模型 Model	$R v 2$	MRE_v	RMSE_v	$R v 2$	MRE_v	RMSE_v
支持向量机Support vector machine	0.886	0.034	0.090	0.785	0.053	0.142
BP神经网络BP neural network	0.887	0.034	0.089	0.820	0.046	0.132

表2 水稻敏感叶位叶片全氮含量估测模型验证结果对比

Table 2 Comparison of validation results of estimation model of total nitrogen content in sensitive leaves of rice

模型 Model	幼穗分化期顶三叶 3rd leaf at the young panicle stage			齐穗期顶二叶 2nd leaf at the full heading stage
模型 Model	$R v 2$	MRE_v	RMSE_v	$R v 2$	MRE_v	RMSE_v
支持向量机Support vector machine	0.886	0.034	0.090	0.785	0.053	0.142
BP神经网络BP neural network	0.887	0.034	0.089	0.820	0.046	0.132

图9 基于机器学习的幼穗分化期顶3叶模型实际值与预测值拟合结果 a,支持向量机模型;b,BP神经网络模型。图10同。

Fig.9 Fitting results of actual value and predicted value of 3rd leaf at the young panicle stage based on machine learning a, Support vector machine model; b, BP neural network model.Same as Figure 10.

图10 基于机器学习的齐穗期顶2叶模型实际值与预测值拟合结果3 讨论

Fig.10 Fitting results of actual value and predicted value of 2nd leaf at the full heading stage based on machine learning

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基于图像特征的水稻叶片全氮含量估测模型研究

Study on estimation model of total nitrogen content in rice leaves based on image characteristics

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