Deep Learning Approach for Classifying Trusses and Runners of Strawberries (original) (raw)

Abstract

The use of artificial intelligence in the agricultural sector has been growing at a rapid rate to automate farming activities. Emergent farming technologies focus on mapping and classification of plants, fruits, diseases, and soil types. Although, assisted harvesting and pruning applications using deep learning algorithms are in the early development stages, there is a demand for solutions to automate such processes. This paper proposes the use of Deep Learning for the classification of trusses and runners of strawberry plants using semantic segmentation and dataset augmentation. The proposed approach is based on the use of noises (i.e. Gaussian, Speckle, Poisson and Salt-and-Pepper) to artificially augment the dataset and compensate the low number of data samples and increase the overall classification performance. The results are evaluated using mean average of precision, recall and F1 score. The proposed approach achieved 91%, 95% and 92% on precision, recall and F1 score, respectively, for truss detection using the ResNet101 with dataset augmentation utilising Salt-and-Pepper noise; and 83%, 53% and 65% on precision, recall and F1 score, respectively, for truss detection using the ResNet50 with dataset augmentation utilising Poisson noise.

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Authors and Affiliations

  1. Computational Intelligence and Applications Research Group, Department of Computer Science, School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
    Jakub Pomykala, Francisco de Lemos, Isibor Kennedy Ihianle, David Ada Adama & Pedro Machado

Authors

  1. Jakub Pomykala
  2. Francisco de Lemos
  3. Isibor Kennedy Ihianle
  4. David Ada Adama
  5. Pedro Machado

Corresponding author

Correspondence toPedro Machado .

Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK
    George Panoutsos
  2. Department of Automatic Control and Systems Engineering, Head of the Intelligent Systems Research Laboratory, The University of Sheffield, Sheffield, South Yorkshire, UK
    Mahdi Mahfouf
  3. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, South Yorkshire, UK
    Lyudmila S Mihaylova

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Pomykala, J., de Lemos, F., Ihianle, I.K., Adama, D.A., Machado, P. (2024). Deep Learning Approach for Classifying Trusses and Runners of Strawberries. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8\_36

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