Detecting mealybug, iron spot and nutrient deficiency by using the physical characteristics of coffee using artificial intelligence

Abstract

The cultivation of coffee in Colombia is essential for the economy and local communities. However, diseases and pests that affect coffee seedlings represent a persistent challenge for production and farmers. Detecting these threats early and accurately is crucial to protecting plant health and ensuring crop quality. In this context, it is proposed to develop an application driven by artificial intelligence that allows detecting and analyzing diseases in coffee leaves.

The goal of this project is to design and create an app that transforms the way farmers address diseases in their coffee crops. Through the integration of artificial intelligence algorithms and neural networks, it seeks to provide farmers with an effective tool that accurately identifies various stages of diseases in coffee seedlings. In addition, the project seeks to detect the presence of the cochineal, a pest that can wreak havoc if not dealt with quickly.