Skip to main content
SpringerLink
Log in

FruitQ: a new dataset of multiple fruit images for freshness evaluation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Application of artificial intelligence methods in agriculture is gaining research attention with focus on improving planting, harvesting, post-harvesting, etc. Fruit quality recognition is crucial for farmers during harvesting and sorting, for food retailers for quality monitoring, and for consumers for freshness evaluation, etc. However, there is a lack of multi-fruit datasets to support real-time fruit quality evaluation. To address this gap, we present a new dataset of fruit images aimed at evaluating fruit freshness, which addresses the lack of multi-fruit datasets for real-time fruit quality evaluation. The dataset contains images of 11 fruits categorized into three freshness classes, and five well-known deep learning models (ShuffleNet, SqueezeNet, EfficientNet, ResNet18, and MobileNet-V2) were adopted as baseline models for fruit quality recognition using the dataset. The study provides a benchmark dataset for the classification task, which could improve research endeavors in the field of fruit quality recognition. The dataset is systematically organized and annotated, making it suitable for testing the performance of state-of-the-art methods and new learning classifiers. The research community in the fields of computer vision, machine learning, and pattern recognition could benefit from this dataset by applying it to various research tasks such as fruit classification and fruit quality recognition. The study achieved impressive results with the best classifier being ResNet-18 with an overall best performance of 99.8% for accuracy. The study also identified limitations, such as the small size of the dataset, and proposed future work to improve deep learning techniques for fruit quality classification tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

The dataset used in this study is available online: FruQ- DB (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7224690 (accessed on 17 October 2022).

References

  1. Abayomi-Alli OO, Damaševičius R, Misra S, Maskeliūnas R (2021) Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning. Expert Syst 38(7):10.1111/exsy.12746

    Article  Google Scholar 

  2. Almadhor A, Rauf HT, Lali MIU, Damaševičius R, Alouffi B, Alharbi A (2021) Ai-driven framework for recognition of guava plant diseases through machine learning from dslr camera sensor based high resolution imagery. Sensors 21(11):10.3390/s21113830

    Article  Google Scholar 

  3. Anowar F, Sadaoui S, Selim B (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40 https://doi.org/10.1016/j.cosrev.2021.100378

  4. Bhargava A, Bansal A (2021) Fruits and vegetables quality evaluation using computer vision: A review. J King Saud Univ - Comput Inform Sci 33(3):243–257. https://doi.org/10.1016/j.jksuci.2018.06.002

    Article  Google Scholar 

  5. Bird JJ, Barnes CM, Manso LJ, Ekárt A, Faria DR (2022) Fruit quality and defect image classification with conditional GAN data augmentation. Sci Hortic 293:110684

    Article  Google Scholar 

  6. Cárdenas-Pérez S, Chanona-Pérez J, Méndez-Méndez JV, Calderón-Domínguez G, López-Santiago R, Perea-Flores MJ, Arzate-Vázquez I (2017) Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system. Biosyst Eng 159:46–58. https://doi.org/10.1016/j.biosystemseng.2017.04.009

    Article  Google Scholar 

  7. Cavallo D, Pietro Cefola M, Pace B, Logrieco AF, Attolico G (2019) Non-destructive and contactless quality evaluation of table grapes by a computer vision system. Comput Electron Agric 156(2018):558–564. https://doi.org/10.1016/j.compag.2018.12.019

    Article  Google Scholar 

  8. Chauhan C, Dhir A, Akram MU, Salo J (2021). Food loss and waste in food supply chains. A systematic literature review and framework development approach. J Clean Prod, 295, 126438. https://doi.org/10.1016/j.jclepro.2021.126438

  9. Chen L, Li S, Bai Q, Yang J, Jiang S, Miao Y (2021) Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens 13:4712. https://doi.org/10.3390/rs13224712

    Article  Google Scholar 

  10. Cho WH, Kim SK, Na MH, Na IS (2021) Fruit ripeness prediction based on DNN feature induction from sparse dataset. Comput, Mater Continua 69(3):4003–4024. https://doi.org/10.32604/cmc.2021.018758

    Article  Google Scholar 

  11. Civille GV, Oftedal KN (2012) Sensory evaluation techniques—Make “good for you” taste “good”. Physiol Behav 107(4):598–605

    Article  Google Scholar 

  12. Das S, Datta S, Chaudhuri BB (2018) Handling data irregularities in classification: Foundations, trends, and future challenges. Pattern Recogn 81:674–693. https://doi.org/10.1016/j.patcog.2018.03.008

    Article  Google Scholar 

  13. Elbir Z, Caferoglu BA, Cihan O (2022) Freshness Grading of Agricultural Products Using Artificial Intelligence. In: Khan M, Khan R, Praveen P (eds) Artificial Intelligence Applications in Agriculture and Food Quality Improvement (pp. 29–54). IGI Global. https://doi.org/10.4018/978-1-6684-5141-0.ch003

    Chapter  Google Scholar 

  14. Fahad LG, Tahir SF, Rasheed U, Saqib H, Hassan M, Alquhayz H (2022) Fruits and vegetables freshness categorization using deep learning. Comput, Mater Continua 71(2):5083–5098. https://doi.org/10.32604/cmc.2022.023357

    Article  Google Scholar 

  15. Fenu G, Malloci FM (2021) DiaMOS plant: A dataset for diagnosis and monitoring plant disease. Agronomy 11(11):10.3390/agronomy11112107

    Article  Google Scholar 

  16. Fu Y, Nguyen M, Yan WQ (2022) Grading methods for fruit freshness based on deep learning. SN Computer. Science 3(4):10.1007/s42979-022-01152-7

    Google Scholar 

  17. Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323–338

    Article  Google Scholar 

  18. He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30, pp. 770–778

  19. Hughes D, Salathé M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.

  20. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: Alex-Net-level Accuracy with 50x Fewer Parameters and <0.5 MB Model Size. arXiv 2019, arXiv:1602.07360

  21. Ismail N, Malik OA (2022) Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Inform Proc Agricult 9(1):24–37. https://doi.org/10.1016/j.inpa.2021.01.005

    Article  Google Scholar 

  22. Javaid M, Haleem A, Rab S, Pratap Singh R, Suman R (2021). Sensors for daily life: A review. In Sensors International (Vol. 2, p. 100121). Elsevier BV. https://doi.org/10.1016/j.sintl.2021.100121

  23. Kang J, Gwak J (2022) Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification. Multimed Tools Appl 81(16):22355–22377. https://doi.org/10.1007/s11042-021-11282-4

    Article  Google Scholar 

  24. Kaur P, Harnal S, Gautam V, Singh MP, Singh SP (2022). An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. Eng Appl Artif Intell, 115, 105210.

  25. Kazi A, Panda SP (2022) Determining the freshness of fruits in the food industry by image classification using transfer learning. Multimed Tools Appl 81(6):7611–7624. https://doi.org/10.1007/s11042-022-12150-5

    Article  Google Scholar 

  26. Komorowski M, Marshall DC, Salciccioli JD, Crutain Y (2016) Exploratory Data Analysis. In: Secondary Analysis of Electronic Health Records. Springer, Cham (CH)

    Google Scholar 

  27. Ma J, Sun D-W, Qu J-H, Liu D, Pu H, Gao W-H, Zeng X-A (2014). Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances. In Crit Rev Food Sci Nutr (Vol. 56, Issue 1, pp. 113–127). https://doi.org/10.1080/10408398.2013.873885

  28. Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA (2022) Application of Artificial Intelligence in Food Industry—a Guideline. Food Eng Rev 14(1):134–175. https://doi.org/10.1007/s12393-021-09290-z

    Article  Google Scholar 

  29. Medhi E, Deb N (2022). PSFD-musa: A dataset of banana plant, stem, fruit, leaf, and disease. Data in Brief, 43 https://doi.org/10.1016/j.dib.2022.108427

  30. Melki P, Bombrun L, Millet E, Diallo B, ElChaoui ElGhor H, Da Costa J-P (2022) Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing. Remote Sens 14:996

    Article  Google Scholar 

  31. Meshram V, Patil K (2022). FruitNet: Indian fruits image dataset with quality for machine learning applications. Data in Brief, 40 https://doi.org/10.1016/j.dib.2021.107686

  32. Meshram V, Thanomliang K, Ruangkan S, Chumchu P, Patil K (2020), "FruitsGB: Top Indian Fruits with quality", IEE.

  33. Nemade SB, Sonavane SP (2020). Co-occurrence patterns-based fruit quality detection for hierarchical fruit image annotation. Journal of King Saud University-Computer and Information Sciences

  34. Ni J, Gao J, Deng, L, Han Z (2020). Monitoring the change process of banana freshness by GoogLeNet. IEEE Access, https://doi.org/10.1109/ACCESS.2020.3045394

  35. Rajbongshi A, Sazzad S, Shakil R, Akter B, Sara U (2022). A comprehensive guava leaves and fruits dataset for guava disease recognition. Data in Brief, 42 https://doi.org/10.1016/j.dib.2022.108174

  36. Rauf HT, Saleem BA, Lali MIU, Khan MA, Sharif M, Bukhari SAC (2019). A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning. Data in Brief, 26 https://doi.org/10.1016/j.dib.2019.104340

  37. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) MobileNet V2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 18–22

  38. Sharma A, Jain A, Gupta P, Chowdary V (2021) Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access 9:4843–4873

    Article  Google Scholar 

  39. Sherafati A, Mollazade K, Saba MK, Vesali F (2022) TomatoScan: An Android-based application for quality evaluation and ripening determination of tomato fruit. Comput Electron Agric 200:107214

    Article  Google Scholar 

  40. Sonwani E, Bansal U, Alroobaea R, Baqasah AM; Hedabou M (2022). An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis. Frontiers in Public Health (Vol. 9). https://doi.org/10.3389/fpubh.2021.816226

  41. Strong DM, Lee YW, Wang RY, (1997) Data quality in context. Commun ACM , 40, 103–110.

  42. Suryawanshi Y, Patil K, Chumchu P (2022). VegNet: Dataset of vegetable quality images for machine learning applications. Data in Brief, 108657.

  43. Tan M, Le QV (2020) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv 2020, arXiv:1905.11946.

  44. Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11)

  45. Wieme J, Mollazade K, Malounas I, Zude-Sasse M, Zhao M, Gowen A, … J. (2022) Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. Biosyst Eng 222:156–176

    Article  Google Scholar 

  46. Xu H, Mao R, Liao H, Zhang H, Lu M, Chen G (2016) Index Based Hidden Outlier Detection in Metric Space. Sci Program 2016:1–14. https://doi.org/10.1155/2016/8048246

    Article  Google Scholar 

  47. Yang J, Luo X, Zhang X, Passos D, Xie L, Rao X, ..., Ying L, (2022). A deep learning approach to improving spectral analysis of fruit quality under interseason variation. Food Control, 109108.

  48. Zarnaq MH, Omid M, Firouz MS, Jafarian M, Bazyar P (2022) Freshness and quality assessment of parsley using image processing and artificial intelligence techniques. CIGR J 24(2):282–290

    Google Scholar 

  49. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23, pp. 6848–6856.

  50. Zhu R, Guo Y, Xue J-H (2020) Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recogn Lett 133:217–223. https://doi.org/10.1016/j.patrec.2020.03.004

    Article  Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Software Engineering, Kaunas University of Technology, 44249, Kaunas, Lithuania

    Olusola O. Abayomi-Alli & Robertas Damaševičius

  2. Department of Applied Data Science, Institute for Energy Technology, 1777, Halden, Norway

    Sanjay Misra

  3. Department of Computer Science, Federal University of Agriculture, Abeokuta, 110124, Nigeria

    Adebayo Abayomi-Alli

Authors
  1. Olusola O. Abayomi-Alli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robertas Damaševičius
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjay Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adebayo Abayomi-Alli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robertas Damaševičius.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abayomi-Alli, O.O., Damaševičius, R., Misra, S. et al. FruitQ: a new dataset of multiple fruit images for freshness evaluation. Multimed Tools Appl 83, 11433–11460 (2024). https://doi.org/10.1007/s11042-023-16058-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16058-6

Keywords

Search

Navigation