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

doi:10.3969/j.issn.1004-1524.20230202

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (3): 651-661.DOI: 10.3969/j.issn.1004-1524.20230202

• Biosystems Engineering • Previous Articles Next Articles

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

Abstract

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

CLC Number:

S652.1
S126

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.

Figures/Tables 12

Fig.1 Schematic diagram of experimental plots

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.

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

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

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

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

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

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^**

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.

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

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

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

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

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