Detecting Surface Defects of Achacha Fruit (Garcinia humilis) with Hyperspectral Images

Abstract

Hyperspectral imaging data within the wavelength range of 400–1000 nm were used to classify the common skin conditions (i.e., normal, scar, decay, and insect bite) of achacha fruits. The band ratio (BR) and spectral angle mapper (SAM) algorithms were used in a binary classification. Furthermore, SAM, support vector machine (SVM), and artificial neural network (ANN) models were used in a multiclass classification. The performances of the binary and multiclass classification models were assessed. For the binary-classification approach, the three defective classes were merged into one, and the accuracies of the BR (990 nm/600 nm) and SAM were 78.70% and 75.02%, respectively. Furthermore, the SAM, SVM, and ANN accuracies in the four class problems were 58.36%, 83.59%, and 99.88%, respectively. A principal component analysis (PCA) was used for the data reduction. Nine characteristic wavelengths were extracted from the weighting-coefficient curves of the first four principal components. Using only the nine selected bands, the accuracies of the SAM, SVM, and ANN models were 51.49%, 80.76%, and 96.85%, respectively. Compared with the models using full bands, the classification accuracies of the models using only nine characteristic bands decreased slightly; however, the gain in classification speed and the potential data-acquisition speed can expedite the classification of achacha fruits.