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Convolutional Neural Networks for Planting System Detection of Olive Groves

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Innovations in Machine and Deep Learning

Part of the book series: Studies in Big Data ((SBD,volume 134))

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Abstract

The present chapter is focused on the identification of different planting systems in olive groves by using very high-resolution aerial orthophotographs through Deep Learning Convolutional Neural Networks techniques. Thus, the DL network proposed classified and discriminated accurately the traditional, intensive and super-intensive management systems. As a starting point in the process, a mini-crop level analysis was performed. To increase the number of standardized samples of the Data Training, a segmentation technique was used to divide the crop images into sub-images (mini-crops), considering different thresholds and stride sizes. These sub-images were discriminated efficiently with accuracies higher than 0.8, showing the biggest image (H = 120 px, W = 120 px) the highest average accuracy (0.957). The super-intensive and traditional managements displayed the most accurate classifications for most of the sub-image sizes. However, major difficulties were found when trying to discriminate intensive systems, with a high degree of confusion with traditional management. Finally, a farm level analysis was also carried out to predict the planting pattern of the entire plantation by identifying the most frequent class of its sub-images. Slightly lower results were observed at farm level, were the image size H = 80 px, W = 80 px obtained the highest accuracy value of 0.826.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, Campus de Rabanales, University of Cordoba, 14071, Córdoba, Spain

    Cristina Martínez-Ruedas

  2. Department of Electronic Engineering, Camino de Los Descubrimientos, S/N, University of Seville, 41009, Sevilla, Spain

    Samuel Yanes Luis & Daniel Gutiérrez Reina

  3. Department of Electric and Automatic Engineering, Campus de Rabanales, University of Cordoba, 14071, Córdoba, Spain

    Juan Manuel Díaz-Cabrera

  4. Department of Rural Engineering, Civil Construction and Engineering Projects, University of Cordoba, 14071, Córdoba, Spain

    Adela P. Galvín

  5. Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, 14071, Córdoba, Spain

    Isabel Luisa Castillejo-González

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  1. Cristina Martínez-Ruedas
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Correspondence to Isabel Luisa Castillejo-González .

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  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Universidad Tecnológica de La Habana, La Habana, Cuba

    Alejandro Rosete

  3. School of Engineering, University of Cadiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Instituto Tecnológico de Ciudad Madero, Tecnológico Nacional de México, Tamaulipas, Mexico

    Nelson Rangel-Valdez

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Martínez-Ruedas, C., Yanes Luis, S., Díaz-Cabrera, J.M., Gutiérrez Reina, D., Galvín, A.P., Castillejo-González, I.L. (2023). Convolutional Neural Networks for Planting System Detection of Olive Groves. In: Rivera, G., Rosete, A., Dorronsoro, B., Rangel-Valdez, N. (eds) Innovations in Machine and Deep Learning. Studies in Big Data, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-031-40688-1_17

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