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Diagnosis of Collateral Effects in Climate Change Through the Identification of Leaf Damage Using a Novel Heuristics and Machine Learning Framework

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Metaheuristics in Machine Learning: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 967))

Abstract

Leaf diseases are very common in plants and can cause a remarkable decrease in the crop production, thus affecting the agricultural economy. It is an abnormal change in the characteristics of plant leaves, such as the presence of lesions and discolorations among others, indicating growth related problems which highly impacts the productivity. The cause of leaf damage can be variable, such as bacteria, virus or as a result of nutritional deficiencies. These symptomatic characteristics of the leaf can be used to classify diseases using image processing and machine learning techniques. This research proposes (i) the creation of a database of images of three types of foliar damage, (ii) the use of image processing methods in the extraction of characteristics and (iii) the combination of assembled algorithms with deep learning to classify foliar features of Valencia (Citrus Sinensis) orange tree leaves, which to our knowledge, have not been studied and reported in existing scientific literature. The results of combining these two classification approaches, show optimal rates in binary datasets and highly competitive percentages in multiclass sets. As a result, it can be shown that the combination of these two classification strategies, can be a highly reliable alternative for orange and other citrus plants. In a near future many Smart Cities will be this problem in their gardens and botanical gardens.

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Acknowledgements

This research was supported by the National Council of Science and Technology of Mexico, grant no. 607316.

Author information

Authors and Affiliations

  1. Departamento de Posgrado, Instituto Tecnológico Superior de Misantla, Veracruz, Mexico

    Juan Salazar & Eddy Sánchez-De La Cruz

  2. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Alberto Ochoa-Zezzatti, Roberto Contreras-Masse & José Mejia

  3. Universidad Politecnica de Aguascalientes, Aguascalientes, Mexico

    Martin Montes

Authors
  1. Juan Salazar
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  2. Eddy Sánchez-De La Cruz
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  3. Alberto Ochoa-Zezzatti
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  4. Martin Montes
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  5. Roberto Contreras-Masse
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  6. José Mejia
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Corresponding author

Correspondence to Alberto Ochoa-Zezzatti .

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Editors and Affiliations

  1. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Diego Oliva

  2. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

  3. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Salvador Hinojosa

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Salazar, J., Sánchez-De La Cruz, E., Ochoa-Zezzatti, A., Montes, M., Contreras-Masse, R., Mejia, J. (2021). Diagnosis of Collateral Effects in Climate Change Through the Identification of Leaf Damage Using a Novel Heuristics and Machine Learning Framework. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_3

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