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

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.