Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm

Abstract

Marketability of agricultural products depends heavily on appearance attributes such as color, size, and ripeness. Sorting plays an important role in increasing marketability by separating crop classes according to appearance attributes, thus reducing waste. As an expert technique, image processing and artificial intelligence (AI) techniques have been applied to classify hawthorns based on maturity levels (unripe, ripe, and overripe). A total of 600 hawthorns were categorized by an expert and the images were taken by an imaging box. The geometric properties, color and, texture features were extracted from segmented hawthorns using the Gray Level Co-occurrence Matrix (GLCM) and evaluation of various color spaces. The efficient feature vector was created by QDA feature reduction method and then classified using two classical machine learning algorithms: Artificial Neural Network (ANN) and Support Vector Machine (SVM). The obtained results indicated that the efficient feature-based ANN model with the configuration of 14–10-3 resulted in the accuracy of 99.57, 99.16, and 98.16% and the least means square error (MSE) of 1 × 10−3, 8 × 10−3, and 3 × 10−3 for training, validation and test phases, respectively. The machine vision system combined with the machine learning algorithms can successfully classify hawthorns according to their maturity levels.