基于图像的幼龄檀香分割与土壤速效氮诊断

doi:10.11707/j.1001-7488.20191208

Abstract

Abstract:

Objectve: In order to ensure the survival rate of sandalwood (Santalum album) and the quality of heartwood in later period, this paper proposed an image segmentation method of sandalwood and a soil available nitrogen nutrition method which was expected to provide a time-saving method to monitor the growth of sandalwood. Method: Converting an image from RGB to a HSI system, then Otsu method to S and I channel was applied in this study. Combining the advantages of the above channels and image filtering method as well as morphological operation method, the sandalwood leaves were segmented from complex background. Using RGB, HSI and Lab systems, the color mean values of leaf images were calculated respectively, and soil available nitrogen content prediction models were built under different fertilization levels and under all levels. For each color system, the mean value of single channel of sandalwood leaves was taken as an independent variable, and the available nitrogen content of each sandalwood tree was taken as a dependent variable to establish a quadratic polynomial of three variables. By calculating the model validation index of fitting data and validation data, the best model was determined. Result: 1) In sandalwood segmentation method under complex background, the S channel could divide the green plants into a whole part, while the I channel could distinguish sandalwood leaves from the other plant leaves. The combination of the two channels could successfully remove most of the background. Combining 7×7 median filter, morphological operation and super G factor, the foreground was extracted more accurately. The pixel number error of this algorithm was within 5%, and the average error of each color channel was controlled within 2%, which showed that the segmentation algorithm was feasible. 2) When building the prediction model of soil available nitrogen content, we compared the prediction result of different color systems. It was found that the Lab system could reflect soil available nitrogen content more accurately under different nitrogen application levels. So dumb variable method was used to build the model, and the prediction model of soil available nitrogen content based on dumb variable was also obtained. At the same time, considering the unknown level of nitrogen application in some forest farms due to neglect of management, this study established a prediction equation at the full level. The result showed that the Lab system was still the best one. The parameter test of the two models presented significant result, indicating that the effects were relatively ideal. Conclusion: The sandalwood leaves could be extracted accurately by combining the characteristics of S channel and I channel in HSI color system under Otsu method, and using median filter, morphological operation and super G factor for post-processing also guaranteed the accuracy. Based on the image color parameters obtained from complex background segmentation, the diagnosis of nitrogen nutrition sandalwood was carried out in this paper. We discovered that the Lab was the best color system regardless of whether nitrogen level was divided or not, and its quadratic polynomial model presented a good prediction ability. The method proposed in this paper could quickly obtain soil nitrogen nutrient content for managers and might ensure the healthy growth of sandalwood.

Key words: sandalwood(Santalum album), image segmentation, nutrition diagnosis, nitrogen, color system

CLC Number:

TP391.41

Zhulin Chen,Xuefeng Wang. Segmentation and Soil Available Nitrogen Diagnosis of Young Stage Sandalwood Based on Image[J]. Scientia Silvae Sinicae, 2019, 55(12): 74-83.

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施氮水平 Nitrogen application level/ (kg·hm^-2)	差异性检验 Difference examination
施氮水平 Nitrogen application level/ (kg·hm^-2)	R	G	B	N/(mg·kg^-1)
0	168.74±4.22^a	186.24±4.38^a	94.14±4.51^a	2.34±1.39^a
50	168.02±4.13^a	185.43±4.27^a	93.87±4.62^a	5.77±2.16^a
100	152.88±5.62^b	172.42±4.51^b	79.74±2.76^b	17.39±6.02^b
150	150.13±5.76^b	168.57±6.08^b	77.84±2.69^b	19.43±7.00^b
200	133.58±5.48^c	159.41±5.36^c	65.91±2.72^c	43.93+14.39^c
250	130.39±5.19^c	157.89±5.72^c	63.83±2.51^c	49.76±16.91^c

施氮水平 Nitrogen application level	速效氮含量Available nitrogen content/ (g·kg^-1)				R通道R channel				G通道G channel				B通道B channel
	最小值	最大值	均值	标准差	最小值	最大值	均值	标准差	最小值	最大值	均值	标准差	最小值	最大值	均值	标准差
	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD	Min.	Max.	Mean	SD
水平1 Level 1	1.20	8.40	4.06	0.67	129.37	184.78	168.38	15.21	152.46	202.61	185.84	15.78	59.68	113.99	94.01	16.27
水平2 Level 2	6.80	28.60	18.41	4.26	120.48	180.85	151.51	20.83	141.51	199.31	170.50	16.89	63.41	92.07	78.79	10.11
水平3 Level 3	22.00	75.10	46.85	17.16	102.28	173.59	131.99	22.11	126.16	183.20	158.65	20.21	58.05	97.60	64.87	11.33
全水平All levels	1.20	75.10	24.21	20.47	102.28	184.78	150.63	21.97	113.99	202.61	171.66	19.09	58.05	113.99	79.22	13.26

处理方法 Method	像素数误差 Pixel number error (%)	R均值 R mean value	误差 Error (%)	G均值 G mean value	误差 Error (%)	B均值 B mean value	误差 Error (%)
M	2.16	143.87	0.23	157.88	0.56	67.75	0.70
PS	2.16	143.54	0.23	157.00	0.56	67.27	0.70
M	2.98	163.70	1.67	183.02	0.36	77.05	1.03
PS	2.98	163.14	1.67	183.68	0.36	77.85	1.03
M	3.67	165.86	0.47	186.16	0.41	79.19	0.94
PS	3.67	166.64	0.47	185.39	0.41	79.94	0.94
M	2.73	184.78	0.66	202.62	0.82	106.48	1.11
PS	2.73	185.99	0.66	204.28	0.82	105.29	1.11
M	4.28	160.92	1.64	193.40	0.47	99.49	1.97
PS	4.28	158.28	1.64	192.48	0.47	101.45	1.97

施氮水平 Nitrogen application level	颜色系统 Color system	预测模型 Prediction model	决定系数 R²	残差方差 δ²	均方根误差 RMSE
水平1 Level 1	RGB	y=C₁R²+C₂R+C₃G²+C₄G+C₅B²+C₆B+C₀	0.73	2.48	1.94
	HSI	y=C₁H²+C₂H+C₃S²+C₄S+C₅I²+C₆I+C₀	0.72	3.95	2.80
	Lab	y=C₁L²+C₂L+C₃a²+C₄a+C₅b²+C₆b+C₀	0.80	0.60	0.76
水平2 Level 2	RGB	y=C₁R²+C₂R+C₃G²+C₄G+C₅B²+C₆B+C₀	0.78	8.94	2.88
	HSI	y=C₁H²+C₂H+C₃S²+C₄S+C₅I²+C₆I+C₀	0.72	10.93	4.19
	Lab	y=C₁L²+C₂L+C₃a²+C₄a+C₅b²+C₆b+C₀	0.81	6.49	2.99
水平3 Level 3	RGB	y=C₁R²+C₂R+C₃G²+C₄G+C₅B²+C₆B+C₀	0.80	85.29	9.02
	HSI	y=C₁H²+C₂H+C₃S²+C₄S+C₅I²+C₆I+C₀	0.73	119.38	15.38
	Lab	y=C₁L²+C₂L+C₃a²+C₄a+C₅b²+C₆b+C₀	0.79	57.93	4.98

施氮水平 Nitrogen application level	颜色系统 Color system	平均误差 ME	平均绝对误差 MAE	平均百分比误差 M%E(%)	平均绝对百分比误差 MA%E(%)	排名 Rank
水平1 Level 1	RGB	0.42	0.69	12.89	16.96	2
	HSI	0.83	1.20	14.05	17.88	3
	Lab	0.47	0.58	12.93	15.46	1
水平2 Level 2	RGB	0.97	2.48	6.91	11.25	2
	HSI	1.46	2.99	7.77	13.64	3
	Lab	0.51	1.81	7.24	10.89	1
水平3 Level 3	RGB	6.82	10.50	14.48	22.49	2
	HSI	13.83	15.38	16.49	25.93	3
	Lab	3.29	7.62	12.96	19.12	1

Segmentation and Soil Available Nitrogen Diagnosis of Young Stage Sandalwood Based on Image

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颜色系统 Color system	预测模型 Prediction model	拟合数据 Modeling data (n=160)			验证数据 Validation data (n=80)
颜色系统 Color system	预测模型 Prediction model	R²	δ²	RMSE	ME	MAE	M%E(%)	MA%E(%)
Lab	y=(C₁K₁+C₂K₂)L²+(C₃K₁+C₄K₂)L+(C₅K₁+C₆K₂)a²+(C₇K₁+C₈K₂)a+(C₉K₁+C₁₀K₂)b²+(C₁₁K₁+C₁₂K₂)b+c₀	0.78	6.54	2.13	1.63	4.18	7.03	17.33

施氮水平 Nitrogen application level	参数估计 Parameter estimation	参数 Parameters
施氮水平 Nitrogen application level	参数估计 Parameter estimation	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉	C₁₀	C₁₁	C₁₂	C₀	—
全水平(哑变量方法) All levels (dumb variable method)	估计值Estimated value	0.04	0.10	-1.95	-43.75	0.60	-0.95	-121.17	190.85	-0.22	0.17	74.70	-56.17	-2.57	—
	标准误Standard error	0.08	0.09	33.41	37.46	0.50	0.55	99.59	108.77	0.11	0.11	36.91	38.92	16.34	—
	P	< 0.001	< 0.001	0.004	0.007	< 0.001	< 0.001	0.014	0.016	< 0.001	< 0.001	0.003	0.002	< 0.001	—

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[5]	Zhu Jialei, Bo Huijuan, Li Xuan, Wen Chunyan, Wang Jiang, Nie Lishui, Tian Ju, Song Lianjun. Long Term Water-Nitrogen Coupling Effect on Stand Volume of Different Clones of Populus tomentosa [J]. Scientia Silvae Sinicae, 2019, 55(5): 27-35.
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