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Exploring Transfer Learning for Enhanced Seed Classification: Pre-trained Xception Model

  • Conference paper
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15th International Congress on Agricultural Mechanization and Energy in Agriculture (ANKAgEng 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 458))

  • 75 Accesses

Abstract

Seed classification plays a crucial role in various agricultural and industrial applications, such as crop breeding, seed quality assessment, and plant disease identification. This study presents a novel deep-learning model for seed classification. In this study, a dataset of 15 seeds has been created, containing around 3018 RGB images, with the objective of develo** an accurate and efficient deep learning-based model capable of classifying seeds with high precision. In this study, we explore the effectiveness of two distinct approaches for seed classification: training the Xception model from scratch and leveraging transfer learning with the Pre-trained Xception model. The experimental results offer a comprehensive comparative analysis of training, validation, and testing outcomes. Notably, the Pre-trained Xception model showcases superior performance across various metrics. It achieves remarkable accuracy, attaining a perfect 1.0000 on both validation and test sets. Additionally, this model demonstrates significantly lower loss values throughout the training phases, highlighting its enhanced predictive capabilities. Impressively, convergence is reached with fewer epochs and in shorter training duration, further underlining the efficiency and effectiveness of the Pre-trained Xception model.

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Acknowledgment

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, under Project GRANT5,362.

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Authors and Affiliations

  1. Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa, 31982, Saudi Arabia

    Yonis Gulzar

  2. Department of Biosystem Engineering, Niğde Ömer Halisdemir University, Central Campus, 51240, Niğde, Turkey

    Zeynep Ünal

  3. Glocal School of Science and Technology, Glocal University, Delhi-Yamunotri Marg (State Highway 57), Mirzapur Pole, Dist - Saharanpur , U.P., 247121, India

    Shahnawaz Ayoub

  4. Department of Computer Science and Information Technology, Jazan University, Jazan, 45142, Saudi Arabia

    Faheem Ahmad Reegu

Authors
  1. Yonis Gulzar
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  2. Zeynep Ünal
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  3. Shahnawaz Ayoub
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  4. Faheem Ahmad Reegu
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Corresponding author

Correspondence to Yonis Gulzar .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Torino, Italy

    Eugenio Cavallo

  2. Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Fernando Auat Cheein

  3. Dipartimento TESAF (University of Padova, TESAF Department), Università di Padova, Legnaro, Italy

    Francesco Marinello

  4. Department of Agricultural Machinery and Technologies Engineering, Ankara University, Altındağ/Ankara, Türkiye

    Kamil Saçılık

  5. Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD, USA

    Kasiviswanathan Muthukumarappan

  6. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Purushothaman C. Abhilash

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Gulzar, Y., Ünal, Z., Ayoub, S., Reegu, F.A. (2024). Exploring Transfer Learning for Enhanced Seed Classification: Pre-trained Xception Model. In: Cavallo, E., Auat Cheein, F., Marinello, F., Saçılık, K., Muthukumarappan, K., Abhilash, P.C. (eds) 15th International Congress on Agricultural Mechanization and Energy in Agriculture. ANKAgEng 2023. Lecture Notes in Civil Engineering, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-031-51579-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-51579-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51578-1

  • Online ISBN: 978-3-031-51579-8

  • eBook Packages: EngineeringEngineering (R0)

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