Non-destructive and in-site estimation of apple quality and maturity by hyperspectral imaging

Abstract

• Hyperspectral imaging monitored the quality of apple directly in orchards. • Relative spectral processing could reduce the unevenness of ambient light. • The accuracy of the prediction models met the requirements of orchard management. • The dynamic process of apple quality and maturity was visualized. Rapid and in-situ estimation of apple quality and maturity is of great significance to guarantee the quality of apples and the profit of the industry. This study explored the utility of hyperspectral imaging (HSI) in assessing the quality and maturity of apples. Hyperspectral images, in the range of 500–900 nm, were collected directly in the orchard. A total of 100 apples contained in the visual field of the camera were picked for destructive testing of firmness, soluble solids content (SSC) and starch pattern index (SPI). To reduce the complicated outdoor light interference, we adopted the PTEE ball as the white reference. Normalized reflectance spectrum (NR) and normalization different spectral index (NDSI) involving all possible two-band combinations were also extracted for apple quality estimation. Subsequently, partial least squares regression (PLSR) analyses were conducted using the characteristic wavelengths extracted by stability competitive adaptive reweighted sampling (SCARS). The PLSR model established by NDSI combined with SCARS achieved the best results. The correlation coefficient of validation set ( R v) of firmness, SSC and SPI were 0.783, 0.901 and 0.834, respectively. The root-mean-square errors of the validation set ( RMSEv ) of firmness, SSC, SPI were 0.993 kg/cm 2 , 0.535% and 0.427, respectively. The Streif index was used to evaluate the maturity of apples, which was calculated from the predicted value of firmness, SSC and SPI. The RMSEv of the PLS model for Streif index was 0.0120. The results indicated the potential of the NDSI-SCARS-PLSR model based on hyperspectral imaging to observe the dynamic changes in the quality and maturity of the apples in site. This model could provide the spatial distribution of apple quality and maturity, which would be valuable for exploring the maturation pattern, grasping the growth of the area, and planning harvest time.