Early Detection of Diseases in Precision Agriculture Processes Supported by Technology


One of the biggest challenges for farmers is the prevention of disease appearance on crops. Governments around the world control border product entry to reduce the number of foreign diseases affecting local producers. Evenmore, it is also important to reduce the spread of crop diseases as quickly as possible and in early stages of propagation, to enable farmers to attack them on time, or to remove the affected plants. In this research, we propose the use of convolutional neural networks to detect diseases in horticultural crops. We compare the results of disease classification in images of plant leaves, in terms of performance, time execution, and classifier size. In the analysis, we implement two distinct classifiers, a densenet-161 pre-trained model and a custom created model. We concluded that for disease detection in tomato crops, our custom model has better execution time and size, and the classification performance is acceptable. Therefore, the custom model could be useful to use to create a solution that helps small farmers in rural areas in resource-limited mobile devices.

Tipo de publicación: Book Chapter

Publicado en: Advances in Sustainability Science and Technology

  • Jose A. Brenes
  • Markus Eger
  • Gabriela Marín-Raventós

Investigadores del CITIC asociados a la publicación
Mag. José Antonio Brenes Carranza
Dra. Gabriela Marín Raventós
Markus Eger

Proyecto asociado a la publicación
Sistema de soporte de decisiones a la agricultura inteligente que incorpore aspectos de automatización de la fertirrigación y recomendaciones al agricultor


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Early Detection of Diseases in Precision Agriculture Processes Supported by Technology