富士和嘎啦苹果叶片面积估算模型构建

doi:10.6048/j.issn.1001-4330.2023.03.017

新疆农业科学 ›› 2023, Vol. 60 ›› Issue (3): 664-674.DOI: 10.6048/j.issn.1001-4330.2023.03.017

• 植物保护·设施农业·农产品加工工程·微生物 • 上一篇下一篇

富士和嘎啦苹果叶片面积估算模型构建

王凯(), 李秀玲, 白茹, 冯建荣, 杨伟伟()

石河子大学农学院特色果蔬栽培生理与种质资源利用兵团重点实验室,新疆石河子 832000

收稿日期:2022-08-21 出版日期:2023-03-20 发布日期:2023-04-18
通信作者: 杨伟伟(1986-),男,山西人,副教授,博士,硕士生导师,研究方向为果树生理生态及模型模拟,(E-mail)yww2567@126.com
作者简介:王凯(1999-),男,安徽人,硕士研究生,研究方向为果树生理生态,(E-mail)2296477313@qq.com
基金资助:
国家自然科学基金(31860527);石河子大学高层次人才科研启动金(RCSX201726);特色果蔬栽培生理与种质资源利用兵团重点实验室开放课题(002)

Construction of Apple Leaf Area Estimation Model

WANG Kai(), LI Xiuling, Fazal Haider, BAI Ru, FENG Jianrong, YANG Weiwei()

Key Laboratory for Oasis Agricultural Pest Management and Plant Protection Resource Utilization / College of Agronomy, Shihezi University, Shihezi Xinjiang 832003,China

Received:2022-08-21 Online:2023-03-20 Published:2023-04-18
Correspondence author: YANG Weiwei(1986-), male, Associate professor PhD, Master supervisor, Research on physiological ecology and model simulation of fruit trees, (E-mail)yww2567@126.com
Supported by:
National Natural Science Foundation of China(31860527);Shihezi University High-level Talent Scientific Research Project(RCSX201726);Special Fruit and Vegetable Cultivation Physiology and Germplasm Resources Utilization Corps for Key Laboratory Open Project(002)

摘要/Abstract

摘要：

【目的】建立准确、无损的适宜于苹果不同品种和枝梢类型的叶面积估算模型。【方法】以富士及嘎啦不同长度的营养枝梢和果台枝梢叶片为试材,采用数字扫描仪获取叶片长度(LL)、宽度(LW)和叶面积(LA)等叶片形态参数,并采用决定系数(R²)、均方根误差(RMSE)、赤池信息准则(AIC)对建立的17个有常数项和无常数项叶面积模型精度进行筛选和适宜性评价。【结果】共获得5 207枚叶片形态参数,其中叶面积变异系数最大,达51.59%。叶片形态受品种及枝梢类型的显著影响,其中长枝梢叶片长、宽和叶面积显著大于同类型短枝梢叶片,而营养枝梢叶片长、宽和叶面积显著大于同长度果台枝梢,嘎啦叶片相比富士更为细长。以LL和LW复合变量为自变量的模型5:LA = a(LL×LW)、模型6:LA = a(LL+LW)²、模型9:LA = aLL²+bLW²、模型16:LA = a(LL×LW)^b、模型17:LA = (LL×LW)^b的精度可满足富士和嘎啦各类枝梢叶面积的估算,但需针对各品种和枝梢类型单独建模。同时包含LL和LW双变量的叶面积模型精度高于单一LL或LW变量的模型。无论富士还是嘎啦,对于不同类型枝梢,果台叶面积模型精度最高,短枝梢均比长枝梢精度高,营养长枝梢拟合精度最低。模型5和17可适用于富士和嘎啦各类枝梢叶面积的估算。模型5和17的R²分别为97.72%和97.37%,RMSE分别为1.756 6和1.831 9 cm²,模型系数分别为0.712 3和0.910 8。满足模型5和17精度所需的最低样品数量为241与3 939枚叶片。【结论】筛选的叶面积模型可用于各品种和各个类型枝梢叶片面积估算,无需针对单个品种及枝梢独立建模,且模型无需添加常数项。叶片数量影响叶面积模型精度,需满足最低临界值。

关键词: 苹果; 叶面积; 模型; 赤池信息准则

Abstract:

【Objective】 This study aims to establish an accurate and non-destructive model for apple leaf area estimation.【Methods】 Leaves were collect from vegetative and bourse shoots considering their length for Fuji and Gala apple trees.Leaf morphological parameters, such as leaf length (LL), leaf width (LW) and leaf area (LA), were obtained by digital scanner.17 candidate models with or without constant were evaluated by determination coefficient (R²), root mean square error (RMSE) and Akakchi Information Criterion (AIC).【Results】 Leaf morphological characteristics of 5,207 leaves were obtained, and among those leaf are had the highest coefficient of variation by 51.59%.Leaf morphologies were significantly affected by shoot types and cultivars The LL, LW and LA of long shoots were significantly larger than those of short shoot, and those were significantly higher in vegetative shoot than bourse shoot when the shoots belong to same category determined by shoot length.Leaves in Gala were more slender than those in Fuji.The accuracies of following selected models where LL and LW were used as independent variables can meet the requirements for leaf area estimation in apple trees, including model 5: LA = a(LL×LW), model 6: LA = a(LL+LW)², model 9: LA = aLL²+bLW², model 16: LA = a(LL×LW)^b, and model 17: LA = (LL×LW)^b., but model need to be established for each shoot type and cultivar.Those models had both LL and LW variables were more reliable than those models only had LL or LW solely.Among shoot types, regarding the accuracy of selected models, bourse was the highest, short shoot was higher than that of long shoot, and vegetative long shoot was the lowest.The all-data model 5 (R²= 97.72%, RMSE = 1.756,6 cm²) and 17 (R²= 97.37%, RMSE =1.831,9 cm²) were available to estimate the leaf area no matter of cultivars or shoot types with same model coefficients.The accuracies of the models 5 and 17 were not improved by adding constant.The model coefficients for model 5 and 17 were 0.712,3 and 0.910,8, respectively.The minimum number of samples was 241 and 3,939 for model 7 and 17, respectively; to have relative errors lower than 5%.【Conclusion】 The selected models in current study can be used for leaf area estimation irrespective of cultivars and shoot types, without the need of establishing model for each type of shoot and cultivar.The addition constant had no effect on selected model accuracy, and there is a minimum number of samples for model were required to have lower relative error.

Key words: apple; leaf area; model; akachi information criteria

中图分类号:

S661.1

王凯, 李秀玲, 白茹, 冯建荣, 杨伟伟. 富士和嘎啦苹果叶片面积估算模型构建[J]. 新疆农业科学, 2023, 60(3): 664-674.

WANG Kai, LI Xiuling, Fazal Haider, BAI Ru, FENG Jianrong, YANG Weiwei. Construction of Apple Leaf Area Estimation Model[J]. Xinjiang Agricultural Sciences, 2023, 60(3): 664-674.

图/表 9

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模型类型 Model types	序号 Order	模型 Model equations	序号 Order	模型 Model equations	变量 Variable
线性模型 Linear models	1	LA = aLL	1c	LA= aLL+c	LL
	2	LA = aLW	2c	LA =aLW+c	LW
	3	LA =aLL²	3c	LA =aLL²+c	LL²
	4	LA = aLW²	4c	LA = a LW²+c	LW²
	5	LA = a (LL×LW)	5c	LA = a (LL×LW)+c	LL×LW
	6	LA = a (LL+LW)²	6c	LA = a (LL+LW)²+c	LL+LW
	7	LA = a (LL+LW)³	7c	LA = a (LL+LW)³+c	LL+LW
	8	LA = aLL+ bLW	8c	LA = aLL+ bLW+c	LL,LW
	9	LA = aLL²+bLW²	9c	LA = aLL²+bLW²+c	LL²,LW²
非线性模型 Non-linear models	10	LA = aLL^b	10c	LA = aLL^b+c	LL
	11	LA =LL^b	11c	LA =LL^b+c	LL
	12	LA = aLW^b	12c	LA = aLW^b+c	LW
	13	LA = LW^b	13c	LA =LW^b+c	LW
	14	LA = a (LL+LW)^b	14c	LA = a (LL+LW)^b+c	LL+LW
	15	LA = (LL+LW)^b	15c	LA = (LL+LW)^b+c	LL+LW
	16	LA = a (LL×LW) ^b	16c	LA = a (LL×LW)^b+c	LL ×LW
	17	LA = (LL×LW) ^b	17c	LA = (LL×LW)^b+c	LL×LW

模型类型 Model types	序号 Order	模型 Model equations	序号 Order	模型 Model equations	变量 Variable
线性模型 Linear models	1	LA = aLL	1c	LA= aLL+c	LL
	2	LA = aLW	2c	LA =aLW+c	LW
	3	LA =aLL²	3c	LA =aLL²+c	LL²
	4	LA = aLW²	4c	LA = a LW²+c	LW²
	5	LA = a (LL×LW)	5c	LA = a (LL×LW)+c	LL×LW
	6	LA = a (LL+LW)²	6c	LA = a (LL+LW)²+c	LL+LW
	7	LA = a (LL+LW)³	7c	LA = a (LL+LW)³+c	LL+LW
	8	LA = aLL+ bLW	8c	LA = aLL+ bLW+c	LL,LW
	9	LA = aLL²+bLW²	9c	LA = aLL²+bLW²+c	LL²,LW²
非线性模型 Non-linear models	10	LA = aLL^b	10c	LA = aLL^b+c	LL
	11	LA =LL^b	11c	LA =LL^b+c	LL
	12	LA = aLW^b	12c	LA = aLW^b+c	LW
	13	LA = LW^b	13c	LA =LW^b+c	LW
	14	LA = a (LL+LW)^b	14c	LA = a (LL+LW)^b+c	LL+LW
	15	LA = (LL+LW)^b	15c	LA = (LL+LW)^b+c	LL+LW
	16	LA = a (LL×LW) ^b	16c	LA = a (LL×LW)^b+c	LL ×LW
	17	LA = (LL×LW) ^b	17c	LA = (LL×LW)^b+c	LL×LW

项目 Item	中位数 Median value	最大值 Maximum value	最小值 Minimum value	平均值 Average value	变异系数 CV(%)
叶片长度 Leaf length(cm)	7.17	15.24(嘎啦营养长枝)	1.50(嘎啦果台)	7.17	31.72
叶片宽度 Leaf width(cm)	4.14	7.99(嘎啦营养长枝)	4.14(嘎啦短果枝)	4.14	26.14
叶柄长度 Petiole length(cm)	2.95	5.46(富士营养短枝)	0.47(富士短果枝)	2.89	29.62
叶片面积 Leaf area(cm²)	21.52	73.94(嘎啦营养长枝)	1.95(嘎啦果台)	22.69	51.59
长宽比 Leaf length/Leaf width ratio	1.71	3.13(嘎啦营养短枝)	0.79(富士果台Bourse)	1.73	19.27
圆度 Roundness	0.13	0.41(富士营养短枝)	0.07(嘎啦营养短枝)	0.14	24.34

项目 Item	中位数 Median value	最大值 Maximum value	最小值 Minimum value	平均值 Average value	变异系数 CV(%)
叶片长度 Leaf length(cm)	7.17	15.24(嘎啦营养长枝)	1.50(嘎啦果台)	7.17	31.72
叶片宽度 Leaf width(cm)	4.14	7.99(嘎啦营养长枝)	4.14(嘎啦短果枝)	4.14	26.14
叶柄长度 Petiole length(cm)	2.95	5.46(富士营养短枝)	0.47(富士短果枝)	2.89	29.62
叶片面积 Leaf area(cm²)	21.52	73.94(嘎啦营养长枝)	1.95(嘎啦果台)	22.69	51.59
长宽比 Leaf length/Leaf width ratio	1.71	3.13(嘎啦营养短枝)	0.79(富士果台Bourse)	1.73	19.27
圆度 Roundness	0.13	0.41(富士营养短枝)	0.07(嘎啦营养短枝)	0.14	24.34

品种 Cultivar	枝梢类型 Shoot type	叶长 Leaf length (cm)	叶宽 Leaf width (cm)	叶柄长 Petiole length (cm)	叶面积 Leaf area (cm²)	叶长/宽比 Leaf length/ Leaf width ratio	圆度 Roundness
富士	果台	3.95^f	3.03^g	1.65^e	9.48^h	1.31^f	0.19^a
Fuji	长果枝	7.00^bc	4.37^b	2.93^b	22.64^d	1.61^cd	0.14^d
	短果枝	6.33^d	3.90^d	2.78^c	19.06^f	1.62^cd	0.15^c
	营养长枝	7.13^bc	4.37^b	2.91^b	24.22^c	1.64^c	0.15^c
	营养短枝	6.56^d	4.05^c	2.86^c	21.48^de	1.60^d	0.16^b
嘎啦	果台	4.77^e	3.20^f	2.09^d	11.60^g	1.49^e	0.16^b
Gala	长果枝	8.26^a	4.40^b	3.25^a	25.99^b	1.89^a	0.11^g
	短果枝	7.28^b	3.84^de	3.30^a	20.63^e	1.90^a	0.12^f
	营养长枝	8.44^a	4.56^a	3.21^a	28.56^a	1.86^ab	0.13^e
	营养短枝	6.94^c	3.71^e	2.65^c	20.11^ef	1.85^b	0.13^e
P值	品种Cultivar	***	ns	***	***	***	***
P values	枝类Shoot type	***	***	***	***	***	***
	品种×枝类	***	***	***	***	**	ns

富士和嘎啦苹果叶片面积估算模型构建

Construction of Apple Leaf Area Estimation Model

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可视化

摘要/Abstract

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图/表 9

参考文献 39

相关文章 15

编辑推荐

Metrics

无常数项模型 Model without constant						有常数项模型 Model with constant
序号 Order	系数a Coefficient a	系数b Coefficient b	决定系数 (R², %)	均方根误差 (RMSE, cm²)	赤池信息准则(AIC)	序号 Order	决定系数 (R², %)	均方根误差 (RMSE, cm²)
1	3.319 5		77.53	5.525 6	3 2681.04	1c	87.33	4.158 4
2	5.785 5		69.67	6.609 6	34 132.65	2c	86.61	4.263 9
3	0.384 1		84.86	4.592 5	30 664.15	3c	88.82	4.032 5
4	1.231 8		87.99	3.963 4	29 250.97	4c	88.75	3.915 5
5	0.712 3		97.72	1.756 6	20 849.11	5c	97.47	1.821 9
6	0.162 3		95.93	2.351 4	23 355.95	6c	96.11	2.232 7
7	0.010 9		71.67	6.306 5	33 012.39	7c	93.23	3.016 0
8	2.570 0	1.300 5	77.40	5.809 2	32 530.78	8c	94.43	2.729 0
9	0.181 9	0.677 1	97.73	1.751 0	20 593.45	9c	98.01	1.672 5
10	0.914 2	1.604 7	87.47	4.294 3	29 403.36	10c	87.89	4.087 9
11	1.566 1		87.74	4.176 8	29 431.18	11c	87.33	4.120 4
12	1.415 5	1.913 4	88.50	4.059 3	29 175.67	12c	88.47	4.058 2
13	2.125 2		87.05	4.311 8	29 582.07	13c	87.72	3.962 5
14	0.213 2	1.895 8	96.65	2.149 1	23 065.05	14c	96.33	2.180 9
15	1.303 2		87.89	4.136 9	29 125.36	15c	95.75	2.485 5
16	0.786 8	0.973 5	97.73	1.763 5	20 768.06	16c	97.88	1.681 9
17	0.910 8		97.32	1.831 9	21 529.06	17c	97.54	1.807 7

Model type	品种 Cultivar	枝梢类型 Shoot type	ΔR² (%)	ΔRMSE (cm²)
模型5 Model 5	富士	果台	1.680 27	-0.093 35
		长果枝	-0.564 54	0.174 67
		短果枝	-0.844 06	0.069 26
		营养长枝	-0.467 96	0.240 24
		营养短枝	-0.615 8	0.115 50
	嘎啦	果台	0.132 41	0.011 47
		长果枝	-0.527 79	-0.059 14
		短果枝	1.086 87	-0.107 73
		营养长枝	0.447 88	-0.269 52
		营养短枝	-1.623 78	0.329 93
模型17 Model 17	富士	果台	-0.103 36	0.059 52
		长果枝	-0.570 06	0.101 34
		短果枝	0.308 63	-0.089 48
		营养长枝	-0.001 54	0.177 94
		营养短枝	-0.159 8	-0.059 36
	嘎啦	果台	-0.783 65	0.223 90
		长果枝	-0.505 73	-0.017 99
		短果枝	-0.630 54	0.192 76
		营养长枝	-0.314 35	0.243 35
		营养短枝	1.696 84	-0.492 21

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