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Tuber Ruler: a mobile application for evaluating image-based potato tuber size

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Abstract

In the potato processing industry, the length-to-width (L/W) ratio of potato tubers is a critical quality indicator, especially for products like French fries and chips. Traditional measurement methods such as manual scales or calipers are labor-intensive and subject to variability. Addressing this challenge, we introduce a mobile application (Tuber Ruler) developed for Android smartphones, which employs image analysis to accurately measure the L/W ratio of potato tubers. By integrating standard image-processing and machine learning (ML) techniques, Tuber Ruler offers a dual-approach analysis, allowing for rapid and precise tuber sizing against both standard black and diverse natural backgrounds. The application exhibits acceptable performance metrics when compared to ground truth measurements obtained via digital calipers. Specifically, Tuber Ruler achieved a Pearson’s correlation coefficient (r) of 0.98 and a mean absolute error (MAE) of 0.03 for russet potatoes, demonstrating consistent accuracy across diverse potato varieties (yellow tubers: r = 0.98, MAE = 0.02; red tubers: r = 0.91, MAE = 0.04). The app can be used in challenging natural environments such as soil and grass backgrounds. Moreover, Tuber Ruler maintains high accuracy (r ≥ 0.99, MAE = 0.01–0.04) even when image resolution is reduced to 25% of the original size, showcasing its resilience to resolution degradation. A significant aspect of Tuber Ruler is the effective use of ML to complement standard image-processing approach, enhancing the application’s adaptability to varied backgrounds and tuber types. This dual-approach analysis, coupled with swift processing times (standard image-processing: 2.0 s; ML: 4.0 s), positions Tuber Ruler as an alternative to traditional sizing methods. By offering a scalable, precise, and user-friendly tool for tuber sizing, Tuber Ruler has the potential to significantly enhance productivity and operational efficiency in the potato industry, becoming a valuable tool to growers, processors and researchers.

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Data availability

The data will be available upon request.

Code availability

The associated codes and APK are publicly released at GitHub: https://github.com/kay795/Potato-Assessment-Application. The application will be launched in Google Play soon.

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Acknowledgements

This research was funded in part by the US Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA) competitive, and hatch and multistate project (Accession Number 1028108, 1014919, NC1212), and Washington State University’s College of Agricultural, Human, and Natural Resource Sciences’ Emerging Research Issues competitive grant opportunity (ERI-20-04).The prototype app was developed as a part of the senior design course, which was further debugged and modified into the current version. The authors would like to thank Dr. Yongsheng Si, College of Information Science and Technology, Hebei Agricultural University, Baoding, Hebei, China, for assisting in the initial development of the image processing algorithm. The authors would also like to thank Dr. Worasit Sangjan, Dr. Afef Marzougui, Milton Valencia-Ortiz, and Kingsley Umani for their assistance in collecting data for app evaluation.

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  1. Kesevan Veloo, Alexander E. Glenn, Alexandra B. King, Bryan J. Smith, Michael M. Marleau and Sindhuja Sankaran have contributed equally to this study.

Authors and Affiliations

  1. Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA

    Kesevan Veloo & Sindhuja Sankaran

  2. School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA, USA

    Alexander E. Glenn, Alexandra B. King, Bryan J. Smith & Michael M. Marleau

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  1. Kesevan Veloo
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Correspondence to Sindhuja Sankaran.

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Veloo, K., Glenn, A.E., King, A.B. et al. Tuber Ruler: a mobile application for evaluating image-based potato tuber size. Food Measure (2024). https://doi.org/10.1007/s11694-024-02542-6

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