Development of Mobile Orchard Local Grading System of Apple Internal Quality

doi:10.12133/j.smartag.SA202206012

Abstract

Abstract:

The detecting and grading of the internal quality of apples is an effective means to increase the added value of apples, protect the health of residents, meet consumer demand and improve market competitiveness. Therefore, an apple internal quality detecting module and a grading module were developed in this research to constitute a movable apple internal quality orchard origin grading system, which could realize the detection of apple sugar content and apple moldy core in orchard origin and grading according to the set grading standard. Based on this system, a multiplicative effect elimination (MEE) based spectral correction method was proposed to eliminate the multiplicative effect caused by the differences in physical properties of apples and improve the internal quality detection accuracy. The method assumed that the multiplication coefficient in the spectrum was closely related to the spectral data at a certain wavelength, and divided the original spectrum by the data at this wavelength point to achieve the elimination of the multiplicative scattering effect of the spectrum. It also combined the idea of least-squares loss function to set the loss function to solve for the optimal multiplication coefficient point. To verify the validity of the method, after pre-processing the apple spectra with multiple scattering correction (MSC), standard normal variate transform (SNV), and MEE algorithms, the partial least squares regression (PLSR) prediction models for apple sugar content and partial least squares-discriminant analysis (PLS-DA) models for apple moldy core were developed, respectively. The results showed that the MEE algorithm had the best results compared to the MSC and SNV algorithms. The correlation coefficient of correction set (Rc), root mean square error of correction set (RMSEC), the correlation coefficient of prediction set (Rp), and root mean square error of prediction set (RMSEP) for sugar content were 0.959, 0.430%, 0.929, and 0.592%, respectively; the sensitivity, specificity, and accuracy of correction set and prediction set for moldy core were 98.33%, 96.67%, 97.50%, 100.00%, 90.00%, and 95.00%, respectively. The best prediction model established was imported into the system for grading tests, and the results showed that the grading correct rate of the system was 90.00% and the grading speed was 3 pcs/s. In summary, the proposed spectral correction method is more suitable for apple transmission spectral correction. The mobile orchard local grading system of apple internal quality combined with the proposed spectral correction method can accurately detect apple sugar content and apple moldy core. The system meets the demand for internal quality detecting and grading of apples in orchard production areas.

Key words: apple, internal quality, visible/near-infrared spectroscopy, spectral correction, nondestructive detecting, grading

CLC Number:

TS251.7

LI Yang, PENG Yankun, LYU Decai, LI Yongyu, LIU Le, ZHU Yujie. Development of Mobile Orchard Local Grading System of Apple Internal Quality[J]. Smart Agriculture, 2022, 4(3): 132-142.

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名称	参数
整体尺寸（长mm×宽mm×高mm）	2000×480×800
光源总功率/W	200
分级速度/（个·s^-1）	约3
分级准确率/%	90
等级信息	特级、一级、二级

等级	糖度/%	霉心病
特级	> 13.7	无
一级	≤13.7	无
二级	/	有

样本集	样本数量/个	最大值/%	最小值/%	均值/%	标准差/%
全部	100	14.30	8.00	11.11	1.56
校正集	75	14.30	8.00	10.98	1.58
预测集	25	14.00	8.40	11.50	1.45

预处理算法	LVs	校正集		预测集
预处理算法	LVs	R_c	RMESC/%	R_p	RMSEP/%
无	12	0.944	0.504	0.912	0.854
SNV	12	0.962	0.413	0.917	0.641
MSC	12	0.958	0.437	0.927	0.601
MEE	12	0.959	0.430	0.929	0.592

预处理算法	LVs	校正集			预测集
预处理算法	LVs	敏感性/%	特异性/%	准确率/%	敏感性/%	特异性/%	准确率/%
无	6	90.00	95.00	92.50	88.33	95.00	87.50
SNV	6	93.33	91.67	92.50	90.00	85.00	87.50
MSC	6	90.00	96.67	93.33	95.00	85.00	90.00
MEE	6	98.33	96.67	97.50	100.00	90.00	95.00