Maturity status classification of papaya fruits based on machine learning and transfer learning approach

Abstract

Papaya (Carica papaya) is a tropical fruit having commercial importance because of its high nutritive and medicinal value. The packaging of papaya fruit as per its maturity status is an essential task in the fruit industry. The manual grading of papaya fruit based on human visual perception is time-consuming and destructive. The objective of this paper is to suggest a novel non-destructive maturity status classification of papaya fruits. The paper suggested two approaches based on machine learning and transfer learning for classification of papaya maturity status. Also, a comparative analysis is carried out with different methods of machine learning and transfer learning. The experimentation is carried out with 300 papaya fruit sample images which includes 100 of each three maturity stages. The machine learning approach includes three sets of features and three classifiers with their different kernel functions. The features and classifiers used in machine learning approaches are local binary pattern (LBP), histogram of oriented gradients (HOG), Gray Level Co-occurrence Matrix (GLCM) and k-nearest neighbour (KNN), support vector machine (SVM), Naïve Bayes respectively. The transfer learning approach includes seven pre-trained models such as ResNet101, ResNet50, ResNet18, VGG19, VGG16, GoogleNet and AlexNet. The weighted KNN with HOG feature outperforms other machine learning-based classification model with 100% of accuracy and 0.099 5 s training time. Again, among the transfer learning approach based classification model VGG19 performs better with 100% accuracy and 1 min 52 s training time with consideration of early stop training. The proposed classification method for maturity classification of papaya fruits, i.e. VGG19 based on transfer learning approach achieved 100% accuracy which is 6% more than the existing method.