Ripeness Classification of Astringent Persimmon Using Hyperspectral Imaging Technique

Nondestructive detection of fruit ripeness is crucial for improving fruits' shelf life and industry production. This work illustrates the use of hyperspectral images at the wavelengths between 400 and 1,000 nm to classify the ripeness of persimmon fruit. Spectra and images of 192 samples were investigated, which were selected from four ripeness stages (unripe, mid-ripe, ripe, and over-ripe). Three classification models-linear discriminant analysis (LDA), soft independence modeling of class analogy, and least squares support vector machines were compared. The best model was LDA, of which the correct classification rate was 95.3 % with the input consisted of the spectra and texture feature of images at three feature wavelengths (518, 711, and 980 nm). Feature wavelengths selection and texture feature extraction were based on successive projection algorithm and gray level co-occurrence matrix, respectively. In addition, using the same input of ripeness detection to make an investigation on firmness prediction by partial least square analysis showed a potential for further study, with correlate coefficient of prediction set r (pre) of 0.913 and root mean square error of prediction of 4.349. The results in this work indicated that there is potential in the use of hyperspectral imaging technique on non-destructive ripeness classification of persimmon. The experimental results could provide the theory support for studying online quality control of persimmon.