Comparison of Various RGB Image Features for Nondestructive Prediction of Ripening Quality of "Alphonso" Mangoes for Easy Adoptability in Machine Vision Applications: A Multivariate Approach

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作者：

V. EYARKAI NAMBI，K. THANGAVEL，S. SHAHIR，V. THIRUPATHI

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摘要：

A study was conducted to predict ripening quality in mangoes using RGB images. Mature "Alphonso" mangoes were selected for experiments. During ripening, random samples were chosen at 24‐h interval for imaging and quality analysis. Hierarchical clustering method was employed to classify the ripening period into five stages based on quality parameters (physico‐chemical, color and textural properties). From each image, 18 features were extracted and evaluated in the prediction of ripening stages. From linear discriminant analysis, group of normalized differential index (NDI) and area features obtained from RGB channels were found better in prediction with 14.9 and 12.3% of misclassification, respectively. While employing quadratic discriminant analysis, NDI and area features predicted the ripening quality effectively with lowest misclassification of 7.9 and 3.5%, respectively. Cross and external validation of selected models had also shown effective results (96.3% correct prediction) with these features. The proposed image features are easy to extract with simple algorithms and useful to predict the ripening quality of mangoes, particularly in machine vision applications.

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关键词：

FRUIT ripening FOOD science periodicals MANGO varieties FOOD preservation HIERARCHICAL clustering (Cluster analysis)

DOI：

10.1111/jfq.12245

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年份：

2016