Image classification of root-trimmed garlic using multi-label and multi-class classification with deep convolutional neural network

Abstract

Garlic root trimming is one of the most tedious tasks in the postharvest processing of garlic since the evaluation of trimming results is done by mainly visual inspection for each label of a garlic bulb. Currently, multi-class classification using a deep convolutional neural network (CNN) can automate the evaluation process. However, it can handle only a single label per a garlic image and cannot be used for evaluation of multi-labels in conventional garlic root trimming practices. This study introduced a modified multi-class model and a multi-label model that utilized CNN to classify two labels of a garlic bulb after root trimming. The first label includes good, bad, untrimmed and scratched classes, and the second label consists of clean and muddy classes. The modified multi-class model achieved a classification accuracy of 82.9% while the multi-label gave a better classification performance of minor classes, with an overall accuracy of 95.2%. With the addition of a background image class, classification accuracies of both multi-class model and multi-label model increased to 91.8% and 98.0%, respectively. The background class significantly enhanced the classification performance of multi-label model when it was deployed to a garlic sorting robot. The utilization of data augmentation, dropout, transfer learning and fine-tuning was confirmed to improve model generalization and performance. Multi-label model is recommended for grading of garlic bulbs with multi-labels of after root trimming. The time to process an image was 0.021 s, which is suitable for a real-time garlic sorting and grading robot. The method shows a potential for development of a smart and fully autonomous robots in the postharvest processing for garlic production.