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Fruit recognition from images using deep learning applications

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Abstract

Smart imaging devices have been used at a rapid rate in the agriculture sector for the last few years. Fruit recognition and classification is noticed as one of the looming sectors in computer vision and image classification. A fruit classification may be adopted in the fruit market for consumers to determine the variety and grading of fruits. Fruit quality is a prerequisite property from a health viewpoint. Classification systems described so far are not adequate for fruit recognition and classification during accuracy and quantitative analysis. Deep learning models have the ability to extract the potential image features without using handcrafted features. In this paper, Type-II Fuzzy, TLBO (Teacher-learner based optimization), and deep learning Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) applications proposed to enhance, segment, recognize and classify the fruit images. Thus, the examination of new proposals for fruit recognition and classification is worthwhile. In the present time, automatic fruit recognition and classification is though a demanding task. Deep learning is a powerful state-of-the-art approach for image classification. This task incorporates deep learning models: CNN, RNN, LSTM for classification of fruits based on chosen optimal and derived features. As preliminary arises, it has been recognized that the recommended procedure has effective accuracy and quantitative analysis results. Moreover, the comparatively high computational momentum of the proposed scheme will promote in the future.

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Authors and Affiliations

  1. Bibi Sharan Kaur Khalsa College, Chamkaur Sahib, (Punjab), India

    Harmandeep Singh Gill

  2. Department of CSE, St. Joseph’s College of Engineering, Chennai, (Tamil Nadu), India

    Ganpathy Murugesan

  3. BAM khalsa College, Garhshankar, (Punjab), India

    Baljit Singh Khehra

  4. Information Technology Department, University of Cumberlands, Kentucky, USA

    Guna Sekhar Sajja

  5. Yogananda School of AI Computers and Data Science, Shoolini University, Solan, India

    Gaurav Gupta

  6. Department of Electronics and Telecommunication, College of Engineering, Pune, India

    Abhishek Bhatt

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  1. Harmandeep Singh Gill
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  2. Ganpathy Murugesan
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  3. Baljit Singh Khehra
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  5. Gaurav Gupta
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Correspondence to Harmandeep Singh Gill.

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Gill, H.S., Murugesan, G., Khehra, B.S. et al. Fruit recognition from images using deep learning applications. Multimed Tools Appl 81, 33269–33290 (2022). https://doi.org/10.1007/s11042-022-12868-2

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  • DOI: https://doi.org/10.1007/s11042-022-12868-2

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