Bruise Detection and Classification of Strawberries Based on Thermal Images
2022
Guo, Bei | Li, Baicheng | Huang, Yuanshen | Hao, Fayi | Xu, Banglian | Dong, Yuanyuan
The detection of bruises plays a vital role in the quality evaluation of strawberries. This study aimed to detect strawberry bruises based on thermal images and classify bruises using a convolutional neural network (CNN). A simple active thermal imaging system was used to capture 2903 thermal images collected from 400 strawberries over 5 days. Moreover, the temperature difference between the bruised area and the unbruised area of the strawberry over time was analyzed. Some of the most advanced pretrained CNN models and the optimized CNN model were evaluated for the classification of unbruised and bruised strawberries based on collected thermal images. The results show that the accuracy of the optimized CNN network is 0.98, which is much higher than the accuracy of the pretrained models. Thus, this study provides a high degree of accuracy in the classification of unbruised and bruised strawberries using the optimized CNN model based on its thermal images, indicating which can be an effective method of detecting and classifying strawberries.
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