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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 132-142.doi: 10.12133/j.smartag.SA202206012

• Special Issue--Key Technologies and Equipment for Smart Orchard • Previous Articles    

Development of Mobile Orchard Local Grading System of Apple Internal Quality

LI Yang1,2(), PENG Yankun1,2(), LYU Decai1,2, LI Yongyu1,2, LIU Le1,2, ZHU Yujie1,2   

  1. 1.College of Engineering, China Agricultural University, Beijing 100083, China
    2.National R& D Center for Agro-processing Equipment, Beijing 100083, China
  • Received:2022-06-28 Online:2022-09-30 Published:2022-11-23
  • corresponding author: PENG Yankun E-mail:158782989@qq.com;ypeng@cau.edu.cn

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: