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SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11508))

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Abstract

Currently, crop management through automatic monitoring is growing momentum, but presents various challenges. One key challenge is to quantify yield traits from images captured automatically. Wheat is one of the three major crops in the world with a total demand expected to exceed 850 million tons by 2050. In this paper we attempt estimation of wheat spikelets from high-definition RGB infield images using a fully convolutional model. We propose also the use of transfer learning and segmentation to improve the model. We report cross validated Mean Absolute Error (MAE) and Mean Square Error (MSE) of 53.0, 71.2 respectively on 15 real field images. We produce visualisations which show the good fit of our model to the task. We also concluded that both transfer learning and segmentation lead to a very positive impact for CNN-based models, reducing error by up to 89%, when extracting key traits such as wheat spikelet counts.

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Author information

Authors and Affiliations

  1. University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK

    Tahani Alkhudaydi, Ji Zhou & Beatriz De La lglesia

  2. Faculty of Computers and IT, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Tahani Alkhudaydi

  3. Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK

    Ji Zhou

  4. Plant Phenomics Research Center, China-UK Plant Phenomics Research Centre, Nanjing Agricultural University, Nanjing, 210095, China

    Ji Zhou

Authors
  1. Tahani Alkhudaydi
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  2. Ji Zhou
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  3. Beatriz De La lglesia
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Corresponding author

Correspondence to Tahani Alkhudaydi .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Alkhudaydi, T., Zhou, J., De La lglesia, B. (2019). SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-20912-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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