Application of convolutional neural networks for the detection of diseases in the CCN-51 cocoa fruit by means of a mobile application.

Abstract

CCN-51 cocoa, one of the two main varieties exported worldwide by Ecuador, due to the lack of technology and poor agronomic practices, is constantly attacked by a number of pests that affect its production, affecting the growth stages of the plant. Another factor that causes damage to the plant is the constant changes in climate, mostly due to excessive rainfall that causes an increase in humidity, damaging the flowering and fruit set, producing Moniliasis as one of its main diseases and being the crops far from the urban area, the analysis is time consuming and very costly, taking as an alternative for most producers the excessive use of chemicals to cure and maintain the pests and diseases of the plant. Where, this research project is proposed, consisting of developing a mobile application that by scanning images in a controlled environment allows the detection of diseases in the CCN-51 cocoa fruit. The mobile application will use its camera to scan the fruit and, using a trained image recognition model, predict a diagnosis of the disease present in the cocoa fruit.