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A deep learning-based framework for accurate identification and crop estimation of olive trees

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A Correction to this article was published on 14 December 2023

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Abstract

Over the last several years, olive cultivation has grown throughout the Mediterranean countries. Among them, Spain is the world’s leading producer of olives. Due to its high economic significance, it is in the best interest of these countries to maintain the crop spread and its yield. Manual enumeration of trees over such extensive fields is impractical and humanly infeasible. There are several methods presented in the existing literature; nonetheless, the optimal method is of greater significance. In this paper, we propose an automated method of olive tree detection as well as crop estimation. The proposed approach is a two-step procedure that includes a deep learning-based classification model followed by regression-based crop estimation. During the classification phase, the foreground tree information is extracted using an enhanced segmentation approach, specifically the K-Mean clustering technique, followed by the synthesis of a super-feature vector comprised of statistical and geometric features. Subsequently, these extracted features are utilized to estimate the expected crop yield. Furthermore, the suggested method is validated using satellite images of olive fields obtained from Google Maps. In comparison with existing methods, the proposed method contributed in terms of novelty and accuracy, outperforming the rest by an overall classification accuracy of 98.1% as well as yield estimate with a root mean squared error of 0.185 respectively.

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Acknowledgements

This study was carried out with the support of R&D Program for Forest Science Technology (Project No.2021338C10-2123-CD02) provided by Korea Forest Service (Korea Forestry Promotion Institute) and supported by the Chung-Ang University Research Grants in 2021.

Funding

Korea Forestry Promotion Institute, No.2021338C10-2123-CD02, Sanghyun Seo.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Islamabad, Pakistan

    Umair Khan, Muazzam Maqsood & Mehr Yahya Durrani

  2. Department of Computer Science, Bahria University, Lahore, Pakistan

    Saira Gillani

  3. Department of Media Design and Technology, Faculty of Engineering & Informatics, University of Bradford, Bradford, UK

    Irfan Mehmood

  4. Chung-Ang University, Seoul, South Korea

    Sanghyun Seo

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  1. Umair Khan
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  4. Mehr Yahya Durrani
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Correspondence to Sanghyun Seo.

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The original online version of this article was revised: In the Acknowledgements section of this article a grant name was incorrectly given as “Chung-Ang University Research Scholarship Grants” and should have been “Chung-Ang University Research Grants”. The original article has been corrected.

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Khan, U., Maqsood, M., Gillani, S. et al. A deep learning-based framework for accurate identification and crop estimation of olive trees. J Supercomput 79, 1834–1855 (2023). https://doi.org/10.1007/s11227-022-04738-3

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