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LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Author

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  • Jorge Parraga-Alava

    (Facultad de Ciencias Informáticas, Universidad Técnica de Manabí, Avenida Jose María Urbina, Portoviejo 130104, Ecuador)

  • Roberth Alcivar-Cevallos

    (Facultad de Ciencias Informáticas, Universidad Técnica de Manabí, Avenida Jose María Urbina, Portoviejo 130104, Ecuador)

  • Jéssica Morales Carrillo

    (Carrera de Computación, Escuela Superior Politécnica Agropecuaria de Manabí, Sitio El Limón, Calceta 130250, Ecuador)

  • Magdalena Castro

    (Facultad de Ciencias Informáticas, Universidad Técnica de Manabí, Avenida Jose María Urbina, Portoviejo 130104, Ecuador)

  • Shabely Avellán

    (Facultad de Ciencias Informáticas, Universidad Técnica de Manabí, Avenida Jose María Urbina, Portoviejo 130104, Ecuador)

  • Aaron Loor

    (Carrera de Computación, Escuela Superior Politécnica Agropecuaria de Manabí, Sitio El Limón, Calceta 130250, Ecuador)

  • Fernando Mendoza

    (Carrera de Computación, Escuela Superior Politécnica Agropecuaria de Manabí, Sitio El Limón, Calceta 130250, Ecuador)

Abstract

Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea . They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.

Suggested Citation

  • Jorge Parraga-Alava & Roberth Alcivar-Cevallos & Jéssica Morales Carrillo & Magdalena Castro & Shabely Avellán & Aaron Loor & Fernando Mendoza, 2021. "LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves," Data, MDPI, vol. 6(5), pages 1-7, May.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:5:p:51-:d:555811
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    References listed on IDEAS

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    1. Marek Stawowy & Stanisław Duer & Jacek Paś & Wojciech Wawrzyński, 2021. "Determining Information Quality in ICT Systems," Energies, MDPI, vol. 14(17), pages 1-18, September.
    2. Miguel A. Becerra & Catalina Tobón & Andrés Eduardo Castro-Ospina & Diego H. Peluffo-Ordóñez, 2021. "Information Quality Assessment for Data Fusion Systems," Data, MDPI, vol. 6(6), pages 1-30, June.
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