Estimation of total nitrogen in young Aquilaria sinensis based on multi image features

doi:10.12302/j.issn.1000-2006.202107017

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3): 19-28.doi: 10.12302/j.issn.1000-2006.202107017

Special Issue: 第三届中国林草计算机应用大会论文精选

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Estimation of total nitrogen in young Aquilaria sinensis based on multi image features

YUAN Ying¹(), WANG Xuefeng¹^,^*(), WANG Tian¹, CHEN Feifei², HUANG Chuanteng², LIN Ling², DONG Xiaona²

1. Research Institute of Forest Resource Information Techniques, CAF, Key Laboratory of Forest Management and Growth Simulation of National Forestry and Grassland Administration, Beijing 100091, China
2. Hainan Academy of Forestry, Hainan Academy of Mangrove, Haikou 571100, China

Received:2021-07-12 Revised:2022-01-07 Online:2023-05-30 Published:2023-05-25
Contact: WANG Xuefeng E-mail:paru_salt@163.com;xuefeng@ifrit.ac.cn

Abstract

Abstract:

【Objective】Multi-image features of young Aquilaria sinensis were extracted using computer vision technology to estimate the total nitrogen content of leaves, providing a new method for rapid and nondestructive measurement of the nitrogen nutritional status of A. sinensis. 【Method】In this study, the best histogram entropy method (KSW entropy method) and morphological processing based on the HIS (Hue-intensity-saturation) color space were used to segment an image of young A. sinensis, and the color, shape and textural features of the image were extracted. Subsequently, the partial least squares (PLS) method was used to reduce the multi-image feature dimensions, and the principal components of the image feature variables were generated. Finally, the Elman neural network (ElmanNN), optimized using the BAS algorithm, was used to estimate the total nitrogen content of young A. sinensis, and the validation results of the model were compared with those of other commonly used models. 【Result】Research showed the following: (1) focusing on the visible image of A. sinensis, the segmentation algorithm based on HIS color space was better than that based on RGB and Lab color space. (2) The PLS algorithm extracted six principal components from the image features, which reduced the dimension of the image features quickly, and effectively eliminated the multicollinearity among the feature variables. (3) The PLS-BAS-ElmanNN model proposed in this study could achieve the adaptive selection of model parameters, and had higher estimation accuracy; for instance, the R² was 0.740 7 and the root mean square error was only 1.265 3 g/kg. The estimation accuracy of it was slightly higher than that of the PLSR and PLS-GAM models. 【Conclusion】In this study, we proposed an image processing method for young A. sinensis and constructed a PLS-BAS-ElmanNN estimation model that can stably process high-dimensional image data. This provides a new idea for monitoring the nitrogen nutrition status of young A. sinensis and has a very important practical significance for the accurate cultivation of A. sinensis.

Key words: Aquilaria sinensis, total nitrogen, computer vision, BAS-Elman, PLS

CLC Number:

S796
TP391

YUAN Ying, WANG Xuefeng, WANG Tian, CHEN Feifei, HUANG Chuanteng, LIN Ling, DONG Xiaona. Estimation of total nitrogen in young Aquilaria sinensis based on multi image features[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(3): 19-28.

Figures/Tables 12

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统计项 statistic	R	G	B	H	S	I	L	a	b
最大值 max	205.869 4	201.039 9	78.221 1	33.596 1	133.920 3	160.001 9	91.481 9	-10.774 0	48.374 4
最小值 min	108.346 9	135.062 6	45.007 0	3.150 8	12.276 4	102.206 7	75.042 1	-18.121 4	25.404 7
平均值 mean	153.145 3	168.051 8	62.698 2	7.901 9	31.357 2	127.965 1	82.767 3	-14.975 3	37.765 6
标准差 SD	24.740 9	16.275 8	8.469 6	5.386 9	21.522 3	14.034 7	4.020 3	1.849 9	6.393 3

统计项statistic	W	H	r	S_a	e
最大值max	2.175 5	4.505 5	1.017 2	1 513.430 1	0.556 7
最小值min	0.800 1	1.095 2	0.345 1	30.061 8	0.085 5
平均值average	1.371 6	2.546 9	0.559 9	357.693 6	0.318 7
标准差SD	0.313 6	0.660 2	0.134 1	274.633 5	0.119 9

统计项 statistic	灰度共生矩阵GLCM								灰度差分统计GLDS
统计项 statistic	能量- 均值 En- mean	能量- 方差 En-std	熵-均值 Ent1- mean	熵-方差 Ent1- std	惯性矩- 均值 MOI- mean	惯性矩- 方差 MOI-std	相关- 均值 Cor- mean	相关- 方差 Cor-std	均值 mean	对比度 Con	角二阶矩 ASM	熵 Ent2
最大值max	0.845 7	0.003 4	1.944 2	0.040 1	0.981 0	0.186 6	0.193 0	0.002 7	0.019 7	524.659 4	0.872 1	2.734 7
最小值min	0.293 5	0.000 5	0.449 8	0.006 6	0.203 8	0.044 3	0.036 0	0.000 1	0.006 6	79.036 0	0.301 2	0.560 4
平均值mean	0.536 7	0.001 6	1.183 2	0.019 7	0.515 1	0.104 0	0.080 5	0.000 4	0.011 6	175.163 6	0.589 9	1.571 7
标准差SD	0.146 7	0.000 6	0.368 2	0.007 3	0.165 7	0.031 9	0.035 9	0.000 4	0.002 6	72.328 6	0.137 4	0.489 0

模型 model		R²	MRE/%	MAE/(g·kg^-1)	MSE/(g²·kg^-2)	RMSE/(g·kg^-1)
非参数模型 non-parametric model	PLS-BAS-ElmanNN	0.740 7	6.0	0.829 2	1.601 0	1.265 3
	PLS-RBFNN	0.720 2	22.8	3.331 4	19.041 1	4.363 6
	PLS-BPNN	0.620 8	18.8	2.858 2	11.186 8	3.344 7
	ElmanNN	0.433 0	15.7	2.495 2	12.428 8	3.525 5
	RBFNN	0.403 0	38.5	5.688 3	40.348 6	6.352 1
	BPNN	0.249 8	30.5	4.605 7	22.334 8	4.726 0
半参数模型 semi-parametric model	PLS-GAM	0.702 3	7.9	1.182 4	2.569 9	1.603 1
参数模型 parametric model	PLSR	0.624 9	8.8	1.297 5	3.016 7	1.736 9
	PCAR	0.428 1	10.5	1.490 1	4.017 3	2.004 3
	RR	0.517 8	10.5	1.518 6	3.821 7	1.954 9

Estimation of total nitrogen in young Aquilaria sinensis based on multi image features

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