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作物学报 ›› 2021, Vol. 47 ›› Issue (8): 1563-1580.doi: 10.3724/SP.J.1006.2021.02063

• 耕作栽培·生理生化 • 上一篇    下一篇

基于小波分析的水稻籽粒直链淀粉含量高光谱预测

张骁1(), 闫岩1, 王文辉1, 郑恒彪1, 姚霞1,2, 朱艳1, 程涛1,2,*()   

  1. 1南京农业大学国家信息农业工程技术中心/江苏省信息农业重点实验室/农业农村部农作物系统分析与决策重点实验室/智慧农业教育部工程研究中心, 江苏南京 210095
    2江苏省现代作物生产协同创新中心, 江苏南京 210095
  • 收稿日期:2020-09-01 接受日期:2021-01-13 出版日期:2021-08-12 网络出版日期:2021-02-25
  • 通讯作者: 程涛
  • 作者简介:E-mail: 13675139398@163.com
  • 基金资助:
    国家重点研发计划项目(2016YFD0300601);国家自然科学基金项目(41871259)

Application of continuous wavelet analysis to laboratory reflectance spectra for the prediction of grain amylose content in rice

ZHANG Xiao1(), YAN Yan1, WANG Wen-Hui1, ZHENG Heng-Biao1, YAO Xia1,2, ZHU Yan1, CHENG Tao1,2,*()   

  1. 1National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing Agricultural University/Jiangsu Key Laboratory for Information Agriculture/Key Laboratory of Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing 210095, Jiangsu, China
    2Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, Jiangsu, China
  • Received:2020-09-01 Accepted:2021-01-13 Published:2021-08-12 Published online:2021-02-25
  • Contact: CHENG Tao
  • Supported by:
    National Key Research and Development Program of China(2016YFD0300601);National Natural Science Foundation of China(41871259)

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摘要：

水稻籽粒直链淀粉含量影响稻米的蒸煮食味品质。利用遥感技术及时、准确地获取籽粒直链淀粉含量可以指导相应栽培措施的制定与实施, 以提高稻米的食味品质。小波分析作为光谱敏感特征提取的有效方法, 广泛应用于作物生理生化参数的估算, 然而基于小波分析的作物品质参数估算, 在米粉、稻穗水平上的应用还未见报道。本文以室内获取的水稻米粉与干穗反射光谱为基础数据源, 通过连续小波光谱变换、敏感小波特征提取、共性特征分析和预测模型构建等4个步骤, 明确不同光谱参数预测水稻籽粒直链淀粉含量的性能, 最终实现在器官水平的直链淀粉含量高光谱预测。结果表明: (1) 相较归一化光谱指数, 敏感小波特征可有效地提高直链淀粉含量预测精度, 预测模型更具普适性和鲁棒性; (2) 从米粉光谱提取的敏感小波特征WF2037,6, 与籽粒直链淀粉含量相关性较高(R2= 0.59), 对独立年份的样本预测效果较好(RMSE = 1.51%, Bias = 0.44%, RRMSE = 23.50%), 并可直接应用于干穗光谱(R2= 0.62, RMSE = 1.49%, Bias = -0.17%, RRMSE = 25.76%)。本文利用连续小波光谱分析, 提取了米粉和稻穗水平的直链淀粉敏感小波特征WF2037,6, 建立了高精度预测模型, 拓宽了连续小波光谱分析的应用范围, 为冠层水平水稻籽粒直链淀粉含量的高光谱估算奠定基础。

关键词: 米粉, 稻穗, 反射光谱, 水稻籽粒直链淀粉含量, 光谱指数, 连续小波光谱分析

Abstract:

Grain amylose content (GAC) is a critical factor affecting the cooking and eating quality of rice. Remote sensing technology can be used to obtain amylose content timely and accurately, which are useful for the establishment and implementation of corresponding cultivation to improve the quality of rice taste. As an effective method for spectral feature extraction, continuous wavelet analysis (CWA) has been widely used to estimate crop physiological and biochemical parameters. However, none of previous studies have used CWA for crop quality estimation and investigated the application of CWA to dried grain powder reflectance spectra acquired in the laboratory for absorption feature extraction. The method was conducted in four steps as below: continuous wavelet transforms, extraction of sensitive wavelet features, analysis of common features, and construction of predictive models. Finally, we estimated GAC on grain scale and compared the performance of different spectral features. The results were as follows: (1) The performance of sensitive wavelet features was better vegetation indices and the independent validation confirmed this superiority; (2) The GAC could be estimated from WF2037,6 with a high R2= 0.59 and the accuracy assessed with validation data from an independent year was RMSE = 1.51%, Bias = 0.44%, RRMSE = 23.50%. The results derived from dried grain powder could be applied to dried panicles (R2= 0.62, RMSE = 1.49%, Bias = -0.17%, RRMSE = 25.76%). This study determined the optimal amylose-sensitive wavelet feature WF2037,6. It can provide new insight into GAC estimation with hyperspectral remote sensing and this method would advance the understanding of rice quality estimation from reflectance spectra at grain and canopy levels.

Key words: rice powder, rice panicle, reflectance spectra, grain amylose content, spectral index, continuous wavelet analysis

表1

米粉及干穗光谱获取数量"

数据集
Data set		 年份
Year		 样本数量
Number of samples		 目的
Objective
米粉光谱Dried grain powder spectra 2018 48 建模集Modeling set
米粉光谱Dried grain powder spectra 2017 16 验证集Validation set
干穗光谱Dried panicle spectra 2018 29 建模集Modeling set
干穗光谱Dried panicle spectra 2018 19 验证集Validation set

图1

米粉光谱测试照片 A: 测试环境; B: 光纤视野场景。"

图2

稻穗光谱测试照片 A: 测试环境; B: 光纤视野场景。"

图3

米粉反射光谱(A)、不同尺度下的小波系数(B)及水稻籽粒直链淀粉敏感小波特征分布图(C)"

图4

水稻叶(A)、茎(B)及穗(C)氮含量随生育期的变化趋势 N0: 施氮量为0 kg hm-2; N1: 施氮量为210 kg hm-2; N2: 施氮量为300 kg hm-2; N3: 施氮量为390 kg hm-2。"

表2

水稻籽粒直链淀粉含量统计特征"

年份
Year		 样本数
No. of samples		 最小值
Min. (%)		 最大值
Max. (%)		 平均值
Mean (%)		 标准差
SD (%)
2017 48 3.13 11.21 6.27 2.19
2018 48 2.10 9.81 5.65 2.21

图5

不同施氮量(A)、播栽方式(B)及品种(C)对水稻籽粒直链淀粉含量的影响 N0: 施氮量为0 kg hm-2; N1: 施氮量为210 kg hm-2; N2: 施氮量为300 kg hm-2; N3: 施氮量为390 kg hm-2。T1: 毯苗移栽; T2: 钵苗移栽。V1: 南粳9108; V2: 甬优2640。"

图6

成熟期水稻器官反射光谱 黑色箭头代表干物质成分的主要吸收波段位置。"

表3

短波红外波段重要化学物质的吸收特征[28]"

波长
Wavelength (nm)		 干物质
Dry matter constituent		 波长
Wavelength (nm)		 干物质
Dry matter constituent
1690 木质素、蛋白质 Lignin, protein 2060 蛋白质、氮 Protein, nitrogen
1820 纤维素 Cellulose 2130 蛋白质 Protein
1900 淀粉 Starch 2180 蛋白质、氮 Protein, nitrogen
1980 蛋白质 Protein 2240 蛋白质 Protein
2000 淀粉 Starch 2300 蛋白质、氮Protein, nitrogen

表4

水稻抽穗后各生育时期米粉或干穗光谱指数与籽粒直链淀粉含量的相关性"

生育时期
Growth stage		 米粉Dried grain powder 干穗Dried panicle
光谱参数
Spectral feature		 决定系数
R2		 光谱参数
Spectral feature		 决定系数
R2
乳熟期Milk stage — — NDAI1760,1585 0.59**
蜡熟期Dough ripening stage NDAI1505,1455 0.61** NDAI1835,1810 0.60**
黄熟期Yellow ripening stage NDAI1345,1210 0.39** NDAI2265,2135 0.70**
完熟期Full ripening stage NDAI1600,1430 0.49** NDAI1290,1175 0.64**

图7

基于NDAI与籽粒直链淀粉含量线性关系的决定系数(R2)等值线图 A: 米粉; B: 干穗。"

图8

水稻籽粒直链淀粉含量与(A)蜡熟期米粉NDAI1505,1455及(B)黄熟期干穗NDAI2265,2135的回归关系图"

图9

米粉水平直链淀粉敏感小波特征分布图 A: 蜡熟期; B: 黄熟期; C: 完熟期; D: 相应波长的米粉反射光谱。蓝色方框代表不同时期存在的相同敏感小波特征。蓝色虚线代表对应波长。黑色箭头代表淀粉吸收波段。"

表5

水稻灌浆后各生育时期米粉小波系数与籽粒直链淀粉含量的相关性"

生育时期
Growth stage		 小波系数
Wavelet feature		 决定系数
R2
蜡熟期 WF1380,3 0.62**
Dough ripening stage WF2037,6 0.59**
黄熟期 WF1812,3 0.43**
Yellow ripening stage WF1550,3 0.36**
完熟期 WF1600,3 0.45**
Full ripening stage WF1375,3 0.30*

图10

水稻籽粒直链淀粉含量与米粉光谱(A) WF1380,3及(B) WF2037,6的回归关系图"

表6

水稻抽穗后各生育期干穗小波系数与籽粒直链淀粉含量的相关性"

生育期Growth stage 小波系数Wavelet feature 决定系数R2
乳熟期 WF1155,6 0.49**
Milk stage WF1895,4 0.49**
蜡熟期 WF1835,3 0.74**
Dough ripening stage WF2350,3 0.64**
黄熟期 WF1692,4 0.67**
Yellow ripening stage WF2030,5 0.65**
完熟期 WF2250,6 0.60**
Full ripening stage WF2050,6 0.55**

图11

干穗水平直链淀粉敏感小波特征分布图 A: 乳熟期; B: 蜡熟期; C: 黄熟期; D: 完熟期; E: 相应波长的米粉反射光谱。蓝色方框代表不同时期存在的相同敏感小波特征。黄色方框代表米粉光谱直链淀粉敏感小波系数。蓝色虚线代表对应波长。黑色箭头代表淀粉吸收波段。"

图12

水稻籽粒直链淀粉含量与干穗光谱(A) WF1835,3及(B) WF2350,3的回归关系图"

图13

米粉、干穗反射光谱和小波系谱对水稻籽粒直链淀粉含量变化的响应 A, B: 不同直链淀粉含量米粉、干穗样品反射光谱; C, D, E, F: 不同直链淀粉含量米粉、干穗样品小波系谱。蓝色箭头代表直链淀粉敏感小波特征对应的波长位置。"

图14

直链淀粉敏感小波特征分布图 A: 米粉; B: 干穗。"

表7

水稻籽粒直链淀粉含量光谱预测精度对照表"

测试器官
Organ of
measurements		 最佳时期
Optimal growth stage		 光谱参数
Spectral feature		 均方根误差
RMSE (%)		 偏移值
Bias (%)		 相对均方根误差
RRMSE (%)
米粉
Dried grain
powder		 蜡熟期
Dough ripening stage		 NDAI1505,1455 3.96 3.30 61.63
WF1380,3 7.73 7.32 120.31
WF2037,6 1.51 0.44 23.50
WF2037,5 3.40 2.14 52.92
干穗
Dried panicle		 NDAI2265,2135 2.17 0.61 37.52
WF1835,3 1.91 -0.84 56.17
WF2037,6 1.49 -0.17 25.76
WF2037,5 2.77 -1.01 81.74

图15

水稻籽粒直链淀粉含量与干穗光谱WF2037,6的回归关系图"

图16

基于光谱指数和小波系数的水稻籽粒直链淀粉含量模型预测值与实测值的1:1关系图 A, B: 米粉NDAI1505,1455, WF2037,6-GAC; C, D: 干穗NDAI2265,2135, WF2037,6-GAC。GAC: 籽粒直链淀粉含量。"

图17

不同生育期米粉及干穗的直链淀粉敏感小波特征分布图及器官反射光谱 米粉A~C: 蜡熟期、黄熟期、完熟期; 干穗D~G: 乳熟期、蜡熟期、黄熟期、完熟期。蓝色方框代表不同时期存在的相同敏感小波系数。蓝色虚线代表对应波长。黑色箭头代表淀粉吸收波段。"

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