Diseases and pests which affect coffee plants are responsible for the reduction in coffee productivity and quality, and also raise production costs and the environmental risks resulting from the application of control measures. The indiscriminate use of agrochemicals also induce pest and disease resistance to control agents. A quick and precise diagnosis and a high or low risk alert for the accelerated progress and establishment of disease and pest epidemics, allied to wide and facilitated access to organized quality information about this field of knowledge, might be decisive in an efficient and effective management scheme. In this context, from available data, essays, and digital images, and with the collaboration and supervision of specialists, the hope is to: obtain models of analysis and of alert for diseases and pests that affect coffee plants with data mining techniques; build a knowledge base for a specialist system of diagnosis for the main diseases; develop an automatic diagnosis method and system through the digital processing of images of disease symptoms; and create organization and representation models about diseases and pests. The expected results are products for the web or for use in mobile devices, with the intention to support studies on coffee plant disease and pest epidemiology, diagnosis, and alerts, in the hopes of reaching higher quality and precision in the evaluation of their incidence in the fields and thus help the elaboration of recommendations that support decision-making for the control of diseases and pests. Analyses of the influence of meteorological conditions on the evolution of pests and diseases will be carried out through the comparison between the past and the present, simulating the effect of future projections of climate change. The desired impact is to improve the integrated management of coffee crop diseases and pests, and it might contribute in the reduction of production costs and environmental impacts of excessive application of agrochemicals.