@article{3337,
  author = {Laura Torres and Luis Romero and Edgar Aguirre and Roberto Ferro-Escobar},
  title = {IoT Detection System for Mildew Disease in Roses Using Neural Networks and Image Analysis},
  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.},
  year = {2023},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {8},
  chapter = {105},
  number = {4},
  pages = {105-116},
  month = {12/2023},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_10.pdf},
  doi = {10.9781/ijimai.2023.07.001},
}