Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging

Sheng Gao; Qiaohua Wang; Dandan Fu; Qingxu Li

doi:10.3788/AOS201939.1030004

Journals >Acta Optica Sinica >Volume 39 >Issue 10 >Page 1030004 > Article

Acta Optica Sinica
Vol. 39, Issue 10, 1030004 (2019)

Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging

Sheng Gao¹, Qiaohua Wang^1、2、*, Dandan Fu¹, and Qingxu Li¹

Author Affiliations

¹College of Engineering, Huazhong Agricultural University, Wuhan, Hubei 430070, China

²Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, Hubei 430070, China

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DOI: 10.3788/AOS201939.1030004 Cite this Article Set citation alerts

Sheng Gao, Qiaohua Wang, Dandan Fu, Qingxu Li. Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging[J]. Acta Optica Sinica, 2019, 39(10): 1030004 Copy Citation Text

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Fig. 1. Hyperspectral images in three placement orientations. (a) Horizontal; (b) fruit stalk-side down; (c) fruit stalk-side up

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Fig. 2. Reflectivity of background and red globe grape area in hyperspectral images

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Fig. 3. Hyperspectral image processing of red globe grapes. (a) Hyperspectral image at 726.6 nm; (b) mask template image; (c) masked image of red globe grape area

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Fig. 4. Originalspectra of red globe grape samples

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Fig. 5. GA characteristic wavelength extraction of sugar content of red globe grape. (a) GA-screened image; (b) change of RMSECV

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Fig. 6. SPA characteristic wavelength extraction of sugar content of red globe grape. (a) Change of RMSE; (b) selected variables of SPA

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Fig. 7. CARS characteristic wavelength extraction of sugar content of red globe grape. (a) Number of sampled variables; (b) RMSECV; (c) paths of regression coefficients

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Fig. 8. UVE characteristic wavelength extraction of sugar content of red globe grape

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Fig. 9. Optimal model for sugar content of red globe grape based on GA-RF

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Fig. 10. Optimal model for firmness of red globe grape based on MA-SPA-RF

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Index	Pretreatment	LVs	Calibration set		Prediction set
Index	Pretreatment	LVs	R_c	RMSEC	R_p	RMSEP
Sugar content	Raw	19	0.827	0.564	0.726	0.474
	SNV	14	0.808	0.597	0.712	0.493
	S_G	2	0.602	0.769	0.483	0.619
	MSC	8	0.811	0.595	0.665	0.515
	MA	8	0.479	0.782	0.373	0.881
	MC	18	0.825	0.617	0.701	0.503
Firmness	RAW	8	0.696	4.743	0.675	4.575
	SNV	6	0.605	5.015	0.684	4.365
	MSC	9	0.685	4.613	0.569	4.789
	MA	16	0.730	4.226	0.808	3.821
	MC	10	0.646	4.652	0.691	4.830

Table 1. Full-band PLSR prediction model using different preprocessing methods

Number of samples	Index	Minimum	Maximum	Mean	Standard deviation
Calibration set (126 samples)	Sugar content /(° Brix)	13.875	18.625	16.109	0.971
Calibration set (126 samples)	Firmness /N	1.200	27.000	13.711	6.213
Prediction set (42 samples)	Sugar content /(° Brix)	15.000	17.5000	15.858	0.653
Prediction set (42 samples)	Firmness /N	2.700	23.500	12.774	6.264

Table 2. Datastatistics of partitioning sample sets by SPXY algorithm

Placement position	Index	LVs	Calibration set		Prediction set
Placement position	Index	LVs	R_c	RMSEC	R_p	RMSEP
Fruit stalk-side down	Sugar content	17	0.807	0.628	0.712	0.488
Fruit stalk-side down	Firmness	3	0.546	5.290	0.534	5.017
Fruit stalk-side up	Sugar content	13	0.792	0.659	0.705	0.492
Fruit stalk-side up	Firmness	6	0.646	4.854	0.606	4.780
Horizontal	Sugar content	19	0.805	0.631	0.690	0.497
Horizontal	Firmness	6	0.558	5.245	0.602	4.785
Whole fruit	Sugar content	19	0.827	0.564	0.726	0.474
Whole fruit	Firmness	19	0.730	4.226	0.808	3.821

Table 3. Full-band PLSR prediction model with different placementorientations

Index	Modeling method	Extraction method	No. of wavelength	Calibration set		Prediction set
Index	Modeling method	Extraction method	No. of wavelength	R_c	RMSEC	R_p	RMSEP
Sugar content	PLSR	Raw	438	0.827	0.564	0.726	0.474
		GA	26	0.875	0.469	0.728	0.443
		SPA	17	0.862	0.492	0.745	0.429
		CARS	24	0.879	0.461	0.753	0.422
		UVE	47	0.863	0.490	0.729	0.444
	LSSVM	Raw	438	0.825	0.568	0.486	0.675
		GA	26	0.870	0.479	0.759	0.415
		SPA	17	0.864	0.489	0.752	0.426
		CARS	24	0.866	0.486	0.810	0.376
		UVE	47	0.875	0.470	0.749	0.426
	RF	Raw	438	0.954	0.260	0.873	0.402
		GA	26	0.969	0.266	0.928	0.254
		SPA	17	0.962	0.268	0.895	0.411
		CARS	24	0.946	0.296	0.890	0.406
		UVE	47	0.961	0.267	0.917	0.297
Firmness	PLSR	MA-Raw	438	0.730	4.226	0.808	3.821
		MA-GA	60	0.802	3.696	0.898	3.273
		MA-SPA	24	0.802	3.699	0.903	2.888
		MA-CARS	22	0.731	4.224	0.886	3.215
		MA-UVE	139	0.804	3.687	0.887	3.114
	LSSVM	MA-Raw	438	0.738	4.224	0.754	4.021
		MA-GA	60	0.795	3.788	0.901	3.023
		MA-SPA	24	0.741	4.183	0.870	3.578
		MA-CARS	22	0.746	4.163	0.893	3.288
		MA-UVE	139	0.833	3.444	0.921	2.674
	RF	MA-Raw	438	0.960	2.195	0.905	3.049
		MA-GA	60	0.950	2.132	0.918	2.031
		MA-SPA	24	0.961	2.119	0.932	1.634
		MA-CARS	22	0.948	2.199	0.911	2.053
		MA-UVE	139	0.959	2.120	0.921	1.893

Table 4. Results of prediction model for sugar content and firmness based on characteristic wavelengths of red globe grape

Index	Modeling method	Selected variables (wavelength) /nm
Sugar content(26 points)	GA-RF	452.76, 456.53, 461.55, 600.98, 626.10, 627.36, 628.62, 631.13, 633.64, 639.92, 644.95, 646.20, 647.46, 648.71,651.23, 655.00, 859.75, 894.92, 918.78, 922.55, 927.58, 936.37, 941.40, 943.91, 945.16, 969.03
Firmness(24 points)	MA-SPA-RF	450.24, 451.50, 454.01, 464.06, 476.62, 489.19, 505.51, 557.02, 677.61, 688.91, 706.50, 825.83, 938.88, 947.68, 952.70, 958.98, 961.49, 962.75, 965.26, 969.03, 977.82, 990.38, 996.67, 997.92

Table 5. Characteristic wave points of optimal model for sugar content and firmness

Sheng Gao, Qiaohua Wang, Dandan Fu, Qingxu Li. Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging[J]. Acta Optica Sinica, 2019, 39(10): 1030004

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