Abstract
Manual sorting of fruits was considered as a significant challenging for agricultural sector as it is a laborious task and may also lead to inconsistency in the classification. In order to improve the apple sorting efficiency and realize the non-destructive testing of apple, the machine vision technology integrated with artificial intelligence was introduced in this article for the design of apple sorting system. This article provides a low budget alternative solution for intelligent grading and sorting of apple fruit employing the deep learning-based approach. The automatic grading of apple was realized according to the determined apple grading standard by applying various stages of artificial intelligence platform like grayscale processing, binarization, enhancement processing, feature extraction and so on. The proposed end-to-end low-cost machine vision system provides an automated sorting of apple and significantly reduces the labor cost and provides a time-effective solution for medium and large-scale enterprises. In order to verify the feasibility of the scheme, the image recognition system of apple sorting machine is tested and the average accuracy of 99.70% is achieved while observing the recognition accuracy 99.38% for the CNN based apple sorting system. The results show that the sorting image recognition system can successfully sort apples according to the perimeter characteristics. It realizes the non-destructive testing and grade classification of apple and provides an important reference value for the research and development of fruit automatic sorting system.
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Yang, M., Kumar, P., Bhola, J. et al. Development of image recognition software based on artificial intelligence algorithm for the efficient sorting of apple fruit. Int J Syst Assur Eng Manag 13 (Suppl 1), 322–330 (2022). https://doi.org/10.1007/s13198-021-01415-1
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DOI: https://doi.org/10.1007/s13198-021-01415-1