Abstract
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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Data Availability
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
Code Availability
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
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Funding
This work is financially supported by the National Natural Science Foundation of China (62005165) and the Key Lab of Intelligent and Green Flexographic Printing (ZBKT201810).
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Guo, B., Li, B., Huang, Y. et al. Bruise Detection and Classification of Strawberries Based on Thermal Images. Food Bioprocess Technol 15, 1133–1141 (2022). https://doi.org/10.1007/s11947-022-02804-5
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DOI: https://doi.org/10.1007/s11947-022-02804-5