基于数字图像的大田哈密瓜不同时期冠层相对叶绿素含量检测研究

doi:10.3969/j.issn.1004-1524.20230202

浙江农业学报 ›› 2024, Vol. 36 ›› Issue (3): 651-661.DOI: 10.3969/j.issn.1004-1524.20230202

基于数字图像的大田哈密瓜不同时期冠层相对叶绿素含量检测研究

刘彦岑^a(), 郭俊先^a^,^*(), 郭阳^a, 史勇^a, 黄华^b, 李龙杰^a, 张振振^a

新疆农业大学 a. 机电工程学院;b. 数理学院,新疆乌鲁木齐 830052

收稿日期:2023-02-20 出版日期:2024-03-25 发布日期:2024-04-09
作者简介:刘彦岑(1998—),男,重庆人,硕士研究生,研究方向为农产品无损检测。E-mail:1015431957@qq.com
通讯作者: *郭俊先,E-mail:junxianguo@163.com
基金资助:
新疆维吾尔自治区高校科研计划自然科学重点项目(XJEDU2020I009);国家自然科学基金(61367001)

Detection of relative chlorophyll content of field cantaloupe canopy at different growth stages based on digital images

LIU Yancen^a(), GUO Junxian^a^,^*(), GUO Yang^a, SHI Yong^a, HUANG Hua^b, LI Longjie^a, ZHANG Zhenzhen^a

a. Mechanical and Electrical Engineering Institute; b. School of Mathematics and Science, Xinjiang Agricultural University, Urumqi 830052, China

Received:2023-02-20 Online:2024-03-25 Published:2024-04-09

摘要/Abstract

摘要：

为探究快速无损获取大田哈密瓜完整冠层相对叶绿素含量的可行性,首先,利用自制的图像采集小车搭载面阵相机,获取不同水肥处理下大田哈密瓜伸蔓期、开花期、膨果期的378张完整冠层图像;然后,对图像进行处理后,提取32种颜色特征和6种纹理特征,分析图像特征与冠层相对叶绿素含量的相关性;接着,对图像特征进行预处理后,选取相应的主成分作为模型输入,分别建立用于预测不同时期冠层相对叶绿素含量的多元线性回归(MLR)、支持向量机回归(SVR)、随机森林(RF)模型。对比建模效果发现,SVR模型的效果最好,在伸蔓期、开花期、膨果期,该模型预测值与实测值回归的决定系数分别为0.73、0.73、0.83,均方根误差分别为0.90、0.91、0.76。研究表明,利用数字图像技术能实现对大田哈密瓜不同时期冠层相对叶绿素含量的快速无损检测,可为大田哈密瓜田间管理提供技术参考。

关键词: 哈密瓜冠层图像, 叶绿素, 图像特征, 支持向量机回归, 关键生育期

Abstract:

To explore the feasibility of quickly and non-destructively obtaining the relative chlorophyll content of the complete canopy of field cantaloupe, a self-made image acquisition vehicle equipped with a plane array camera was used in the present study to collect a total of 378 complete canopy images of field cantaloupe during the vine stretching stage, the flowering stage, and the fruit expansion stage, under different water and fertilizer treatments. After image processing, 32 color features and 6 texture features were extracted, and the correlation between image features and relative chlorophyll content of canopy was analyzed. After image features being preprocessed, principal components were selected as model inputs to establish multiple linear regression (MLR), support vector regression (SVR), and random forest (RF) prediction models for the relative chlorophyll content of field cantaloupe canopy at different growth stages. By comparison, it was found that the SVR models had the best performance, as the determination coefficients of the regression within the predicted values and the measured values were 0.73, 0.73 and 0.83, respectively, and the root mean squre errors were 0.90, 0.91 and 0.76 for the vine stretching stage, the flowering stage and the fruit expansion stage, respectively. It was proved that digital image technology may enable rapid and non-destructive detection of relative chlorophyll content of the field cantaloupe canopy at different growth stages, which could provide technical references for the field management of cantaloupe production.

Key words: canopy image of cantaloupe, chlorophyll, image features, support vector regression, critical growth period

中图分类号:

S652.1
S126

刘彦岑, 郭俊先, 郭阳, 史勇, 黄华, 李龙杰, 张振振. 基于数字图像的大田哈密瓜不同时期冠层相对叶绿素含量检测研究[J]. 浙江农业学报, 2024, 36(3): 651-661.

LIU Yancen, GUO Junxian, GUO Yang, SHI Yong, HUANG Hua, LI Longjie, ZHANG Zhenzhen. Detection of relative chlorophyll content of field cantaloupe canopy at different growth stages based on digital images[J]. Acta Agriculturae Zhejiangensis, 2024, 36(3): 651-661.

图/表 12

图1 试验小区示意图

Fig.1 Schematic diagram of experimental plots

图2 冠层图像采集装置示意图 1,试验地;2,导轨;3,挡光铝箔;4,光源开关;5,镜头;6,面阵相机;7,图像采集小车;8,电脑;9,固定光源;10,冠层叶片。

Fig.2 Schematic diagram of canopy image acquisition device 1, Test site; 2, Guide rail; 3, Light blocking aluminum foil; 4, Light source switch; 5, Lens; 6, Area array camera; 7, Image acquisition car; 8, Computer; 9, Fixed light source; 10, Canopy blade.

表1 颜色特征的基本信息

Table 1 Brief introduction of color features

特征来源 Features source	数量 Number	特征参数 Feature parameters
RGB	22	R、G、B、R-B、G-R、G-B、G/R、B/G、B/R、R/(R+B+G)、G/(R+B+G)、B/(R+B+G)、1.4R-G、(R-B)/(R+B)、(G-B)/(G+B)、(G-R)/(G+R)、2G-R-B、(2G-R-B)/(2G+R+B)、(G²-RB)/(G²+RB)、3G-2.4R-B、(G²-R²)/(G²+R²)、0.44R-0.88G-0.39B+18.787 5
Gray	1	G_ray
HSV	3	H、S、V
NTSC	3	Y_NTSC、I、Q
YCbCr	3	Y、C_b、C_r

表2 纹理特征的基本情况

Table 2 Brief introduction of texture features

纹理特征名称 Texture feature name	符号 Symbol	表达式 Expression	纹理度量 Texture measurement
平均值 Mean	m	$m = ∑ i = 0 L - 1 z i p (z i)$	平均亮度 Average brightness
标准偏差 Standard deviation	S	$S = ∑ i = 0 L - 1 (z i - m) 2 p (z i)$	平均对比度 Average contrast
平滑度 Smoothness	F	$F = 1 - 1 / (1 + S 2)$	区域中亮度的相对平滑度 Relative smoothness of brightness in the area
三阶矩 Third moment	T³	$T 3 = ∑ i = 0 L - 1 (z i - m) 3 p (z i)$	直方图偏斜程度 Degree of histogram deviation
一致性 Consistency	U	$U = ∑ i = 0 L - 1 p 2 (z i)$	灰度值一致性 Consistency of gray value
熵 Entropy	e	$e = - ∑ i = 0 L - 1 p (z i) l o g 2 p (z i)$	灰度级混乱程度 Grayscale confusion

表2 纹理特征的基本情况

Table 2 Brief introduction of texture features

纹理特征名称 Texture feature name	符号 Symbol	表达式 Expression	纹理度量 Texture measurement
平均值 Mean	m	$m = ∑ i = 0 L - 1 z i p (z i)$	平均亮度 Average brightness
标准偏差 Standard deviation	S	$S = ∑ i = 0 L - 1 (z i - m) 2 p (z i)$	平均对比度 Average contrast
平滑度 Smoothness	F	$F = 1 - 1 / (1 + S 2)$	区域中亮度的相对平滑度 Relative smoothness of brightness in the area
三阶矩 Third moment	T³	$T 3 = ∑ i = 0 L - 1 (z i - m) 3 p (z i)$	直方图偏斜程度 Degree of histogram deviation
一致性 Consistency	U	$U = ∑ i = 0 L - 1 p 2 (z i)$	灰度值一致性 Consistency of gray value
熵 Entropy	e	$e = - ∑ i = 0 L - 1 p (z i) l o g 2 p (z i)$	灰度级混乱程度 Grayscale confusion

表3 不同处理下大田哈密瓜的冠层相对叶绿素含量

Table 3 Relative chlorophyll content of field cantaloupe canopy under treatments

处理 Treatment	样本数 Sample size	各时期的相对叶绿素含量Relative chlorophyll content at different growth stages
处理 Treatment	样本数 Sample size	伸蔓期 Vine stretching stage	开花期 Flowering stage	膨果期 Fruit expansion stage
W₁N₁	18	36.98±2.27 d	44.83±2.33 e	52.68±2.64 d
W₁N₂	18	37.14±2.43 cd	45.94±3.01 de	54.03±2.82 bcd
W₁N₃	18	36.72±3.10 d	45.44±1.85 de	53.77±3.28 cd
W₂N₁	18	39.18±2.22 ab	47.49±2.08 bc	54.85±2.31 abc
W₂N₂	18	39.84±2.63 a	49.37±1.87 a	56.27±2.93 a
W₂N₃	18	39.64±2.58 ab	47.92±2.82 b	55.56±2.73 ab
CK	18	38.34±2.67 bc	46.55±2.57 cd	54.89±2.56 abc

图3 原始图像及经过处理的大田哈密瓜冠层图像

Fig.3 Images of field cantaloupe canopy before and after image processing

表4 不同时期图像特征与冠层相对叶绿素含量的相关系数

Table 4 Correlation coefficient between image features and relative chlorophyll content of field cantaloupe canopy at different growth stages

图像特征 Image features	不同时期的相关系数Correlation coefficient at different growth stages
图像特征 Image features	伸蔓期Vine stretching stage	开花期Flowering stage	膨果期Friut expansion stage
R	0.72^**	0.69^**	0.84^**
G	0.73^**	0.79^**	0.80^**
B	0.73^**	0.18	0.38^*
R-B	0.69^**	0.61^**	0.68^**
G-R	0.73^**	0.86^**	0.55^**
G-B	0.72^**	0.78^**	0.82^**
G/R	0.46^**	0.29	-0.12
B/G	0.25	0.42^*	0.11
B/R	0.13	-0.35	-0.38^*
R/(R+B+G)	-0.42^**	0.24	0.28
G/(R+B+G)	0.27	0.38^*	0.37^*
B/(R+B+G)	0.01	-0.36^*	-0.41^**
(R-B)/(R+B)	-0.13	0.35	0.38^*
(G-B)/(G+B)	0.06	0.37^*	0.42^**
(G-R)/(G+R)	0.46^**	0.31	-0.11
(G²-RB)/(G²+RB)	0.24	0.38^*	0.41^*
2G-R-B	0.72^**	0.83^**	0.79^**
1.4R-G	-0.70^**	-0.60^**	-0.02
3G-2.4R-B	0.72^**	0.82^**	0.70^**
(G²-R²)/(G²+R²)	0.46*	0.3	-0.1
(2G-R-B)/(2G+R+B)	0.26	0.21	0.11
0.44R-0.88G-0.39B+18.787 5	-0.73^**	-0.72^**	-0.70^**
G_ray	0.73^**	0.65^**	0.78^**
H	0.73^**	0.74^**	0.17
S	0.72^**	0.63^**	0.47^**
V	0.73^**	0.79^**	0.80^**
Y_NTSC	0.73^**	0.75^**	0.81^**
I	-0.62^**	0.13	0.30
Q	-0.72^**	-0.82^**	-0.80^**
Y	0.73^**	0.75^**	0.81^**
C_b	-0.72^**	-0.75^**	-0.81^**
C_r	-0.72^**	-0.82^**	-0.80^**
m	0.73^**	0.75^**	0.81^**
S	0.53^**	-0.24	0.17
F	0.52^**	-0.24	0.16
T³	-0.65^**	-0.33^*	-0.22
U	-0.73^**	-0.75^**	-0.31^*
e	0.73^**	0.77^**	0.47^**

图4 不同时期图像特征及冠层相对叶绿素含量的相关性 R、G、B分别表示RGB模型的红、绿、蓝三基色;Gray表示灰度模型的灰度值;H、S、V分别表示HSV模型的色调、色饱和度和明度;YNTSC、I、Q分别表示NTSC模型的光亮度、色调、饱和度;Y、Cb、Cr分别表示YcbCr模型的亮度分量、蓝色色度分量、红色色度分量;m,平均值;S,标准偏差;F,平滑度;T3,三阶矩;U,一致性;e,熵;D,相对叶绿素含量。“*”表示相关性达到显著水平(P<0.05)。

Fig.4 Correlation of image features and relaitve chlorophyll content of field cantaloupe canopy at different growth stages R, G, B represent the red, green and blue primary colors of RGB model, respectively; Gray denotes the gray value of the grayscale model ;H, S, V represent the hue, color saturation and value of HSV model, respectively; YNTSC, I, Q represent the brightness, hue and saturation of NTSC model, respectively; Y, Cb, Cr represent the luminance component, blue chromaticity component and red chromaticity component of YcbCr model, respectively; m, Mean; S, Standard deviation; F, Smoothness; T3, Third moment; U, Consistency; e, Entropy; D, Relative chlorophyll content of canopy. “*” indicate significant correlation at P<0.05.

表5 主成分的方差贡献率

Table 5 Variance contribution rate of principal components

主成分 Principle component	各时期的方差贡献率 Variance contribution rate at different growth stages			各时期的累积方差贡献率 Cumulative variance contribution rate at different growth stages
主成分 Principle component	伸蔓期 Vine stretching stage	开花期 Flowering stage	膨果期 Fruit expansion stage	伸蔓期 Vine stretching stage	开花期 Flowering stage	膨果期 Fruit expansion stage
P1	0.562 1	0.504 4	0.517 7	0.562 1	0.504 4	0.517 7
P2	0.169 3	0.245 1	0.206 8	0.731 4	0.749 5	0.724 5
P3	0.104 2	0.117 3	0.104 4	0.835 6	0.866 8	0.828 9
P4	0.071 1	0.062 6	0.082 3	0.906 7	0.929 4	0.911 2
P5	0.040 7	0.033 7	0.044 6	0.947 4	0.963 1	0.955 8
P6	0.020 4	0.012 4	0.022 5	0.967 8	0.975 5	0.978 3
P7	0.010 6	0.008 8	0.009 6	0.978 4	0.984 3	0.987 9
P8	0.008 8	0.005 2	0.004 8	0.987 2	0.989 5	0.992 7
P9	0.007 3	0.003 8	0.003 6	0.994 5	0.993 3	0.996 3
P10	0.004 3	0.003 2	0.003 3	0.998 8	0.996 5	0.999 6

表6 建模参数表

Table 6 Modeling parameters

模型 Model	模型参数 Model parameter	各时期的参数值Parameter value at different growth stages
模型 Model	模型参数 Model parameter	伸蔓期 Vine stretching stage	开花期 Flowering stage	膨果期 Fruit expansion stage
MLR	β₀	40.004	42.621	47.692
	β₁	0.538	0.471	0.685
	β₂	-0.167	0.612	-0.623
	β₃	0.042	0.147	-0.553
	β₄	0.151	0.035	0.237
	β₅	0.738	0.175	-1.048
SVM	C	64	42	26
	g	0.2	0.1	0.1
RF	ntree	300	200	200
	mtry	3	3	2

表7 建模效果对比

Table 7 Comparison of modeling effect

模型 Model	时期 Period	RMSE		R²
模型 Model	时期 Period	训练集Training set	测试集Test set	训练集Training set	测试集Test set
MLR	伸蔓期Vine stretching stage	1.72	2.52	0.70	0.56
	开花期Flowering stage	1.61	1.78	0.69	0.61
	膨果期Fruit expansion stage	1.78	2.12	0.74	0.72
SVR	伸蔓期Vine stretching stage	0.92	0.97	0.78	0.71
	开花期Flowering stage	0.92	0.93	0.80	0.77
	膨果期Fruit expansion stage	0.66	0.73	0.83	0.81
RF	伸蔓期Vine stretching stage	0.91	1.03	0.79	0.75
	开花期Flowering stage	1.28	1.32	0.77	0.76
	膨果期Fruit expansion stage	0.81	0.99	0.81	0.78

图5 不同时期支持向量机回归(SVR)模型预测值与实测值的回归散点图 RMSE,均方根误差;R2,决定系数。

Fig.5 Regression scatter diagram of the predicted value by support vector regression (SVR) model and the measure value at different growth stages RMSE, Root mean square error; R2, Coefficient of determination.

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基于数字图像的大田哈密瓜不同时期冠层相对叶绿素含量检测研究

Detection of relative chlorophyll content of field cantaloupe canopy at different growth stages based on digital images

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