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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (8): 1563-1580.doi: 10.3724/SP.J.1006.2021.02063

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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 Online:2021-08-12 Published:2021-02-25
  • Contact: CHENG Tao E-mail:13675139398@163.com;tcheng@njau.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2016YFD0300601);National Natural Science Foundation of China(41871259)

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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

Table 1

Number of samples for the spectral measurements of dried grain powder and panicles"

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

Fig. 1

Laboratory measurements of spectral reflectance over dried grain powder A: testing system; B: visual field of sensor."

Fig. 2

Laboratory measurements of spectral reflectance over dried rice panicles A: testing system; B: visual field of sensor."

Fig. 3

Dried grain powder reflectance spectra (A), wavelet coefficient spectra at different scales (B), and correlation scalogram for the identification of significant wavelet features related to GAC (C)"

Fig. 4

Change trend of nitrogen content in leaf (A), stem (B), and panicle (C) during multiple growth stages N0: 0 kg hm-2 of N fertilizer; N1: 210 kg hm-2 of N fertilizer; N2: 300 kg hm-2 of N fertilizer; N3: 390 kg hm-2 of N fertilizer."

Table 2

Statistical characteristics of GAC in rice"

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

Fig. 5

Effects of different nitrogen application rates, planting techniques, and rice varieties on grain amylose content in rice N0: 0 kg hm-2 of N fertilizer; N1: 210 kg hm-2 of N fertilizer; N2: 300 kg hm-2 of N fertilizer; N3: 390 kg hm-2 of N fertilizer. T1: blanket seedling transplanting; T2: tray seedling transplanting. V1: Nanjing 9108; V2: Yongyou 2640."

Fig. 6

Reflectance spectra of rice organs under the same treatment at full ripening stage The black arrows downward indicate the wavelength locations of absorption features for dry matter constituents."

Table 3

Absorption features that have been related to particular chemical constituents in the shortwave infrared region [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

Table 4

Coefficient of determination (R2) values for the relationships of NDAI with GAC at post-heading stages in rice"

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**

Fig. 7

Contour maps of R2 for the linear relationships between NDAI and GAC A: dried grain powder; B: dried panicle."

Fig. 8

Relationships between GAC and NDAI determined from (A) dried grain powder at dough ripening stage and (B) dry panicles at yellow ripening stage"

Fig. 9

Correlations scalograms for the identification of significant wavelet features of dried grain powder related to GAC A: dough ripening stage; B: yellow ripening stage; C: full ripening stage; D: reflectance spectra of dried grain powder. The blue box represents the same sensitive wavelet features at different growth stages, while the blue dashed lines represent the corresponding wavelengths. The black downward arrows on the top of the figure indicate the wavelength locations of absorption features for starch."

Table 5

Coefficient of determination (R2) values for the relationships of wavelet features determined from dried grain powder spectra with GAC at post-filling stages"

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*

Fig. 10

Relationships between GAC and the wavelet features (A: WF1380,3; B: WF2037,6) determined from dried grain powder spectra"

Table 6

Coefficient of determination values (R2) for the relationships of wavelet features determined from dry panicle spectra with GAC of rice at post-heading stages"

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**

Fig. 11

Correlation scalograms for the identification of significant wavelet features of dried panicle related to GAC A: milking stage; B: dough ripening stage; C: yellow ripening stage; D: full ripening stage; E: reflectance spectra of rice panicles. The blue boxes represent the same sensitive wavelet features at different growth stages, while the blue dashed lines represent the corresponding wavelengths. The yellow box represents GAC sensitive wavelet coefficient determined at powder level. The black downward arrows on the top of the figure indicate the wavelength locations of absorption features for starch."

Fig. 12

Relationships between GAC and wavelet features (A: WF1835,3; B: WF2350,3) determined from dried panicle spectra"

Fig. 13

Spectral responses of dried grain powder and dried panicle to GAC in reflectance and wavelet power A-B: reflectance of dried grain powder and dried panicle with different GAC values; C-F: wavelet features of dried grain powder and dried panicles with different GAC values. The blue arrows represent the wavelength locations corresponding to the sensitive wavelet features of amylose."

Fig. 14

Correlation scalograms for the identification of significant wavelet features related to GAC A: dried grain powder; B: dried panicle."

Table 7

Assessment of prediction accuracies for rice GAC with determined spectral indexes and wavelet features"

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

Fig. 15

Relationship between the wavelet feature WF2037,6 and GAC in rice at dry panicle level"

Fig. 16

Relationships between predicted and measured GAC based on NDAI or wavelet features A-B: dried grain powder NDAI1505,1455, WF2037,6-GAC; C-D: dried panicle NDAI2265,2135, WF2037,6-GAC. GAC: grain amylose content."

Fig. 17

Correlation scalograms for the identification of significant wavelet features related to GAC and reflectance spectra of rice organs Dried grain powder A-C: dough ripening stage, yellow ripening stage, full ripening stage; dry panicle D-G: milking stage, dough ripening stage, yellow ripening stage, full ripening stage. The blue boxes represent the same sensitive wavelet features at different growth stages, while the blue dashed lines represent the corresponding wavelengths. The black downward arrows on the top of the figure indicate the wavelength locations of absorption features for starch."

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. Monitoring Leaf Nitrogen Accumulation with Canopy Spectral Reflectance in Rice

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