IoT Detection System for Mildew Disease in Roses Using Neural Networks and Image Analysis.

Laura Torres; Luis Romero; Edgar Aguirre; Roberto Ferro

doi:10.9781/ijimai.2023.07.001

Authors

Laura Torres Corporación Universitaria Minuto de Dios
Luis Romero Corporación Universitaria Minuto de Dios
Edgar Aguirre Corporación Universitaria Minuto de Dios
Roberto Ferro Universidad Distrital Francisco José de Caldas

DOI:

https://doi.org/10.9781/ijimai.2023.07.001

Abstract

Artificial intelligence presents different approaches, one of these is the use of neural network algorithms, a particular context is the farming sector and these algorithms support the detection of diseases in flowers, this work presents a system to detect downy mildew disease in roses through the analysis of images through neural networks and the correlation of environmental variables through an experiment in a controlled environment, for which an IoT platform was developed that integrated an artificial intelligence module. For the verification of the model, three different models of neural networks in a controlled greenhouse were experimentally compared and a proposed model was obtained for the training and validation sets of two categories of healthy roses and diseased roses with 89% training and 11% recovery. validation and it was determined that the relative humidity variable can influence the development and appearance of Downy Mildew disease when its value is above 85% for a prolonged period.

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