Crop pests are among the greatest threats to food security, generating broad economic, social, and environmental impacts. These pests interact with their hosts and the environment through complex pathways, and it is increasingly common to find professionals from different areas gathering into projects that attempt to deal with this complexity. We propose a framework called FramePests guiding steps and activities for crop pest modeling and forecasting. From theoretical references about carrying out mappings and systematic reviews of the literature, the framework proposes a series of steps leading to a state of science as a knowledge base for modeling tasks. Then, two modeling solutions, based on data and knowledge are used. Finally, the model outputs and performances are compared. The application of the proposed framework was demonstrated for coffee leaf rust modeling, for which we obtained a data-based model built using a gradient boosting algorithm (XGBoost) with a mean absolute error of 7.19% and a knowledge-based model represented by a hierarchical multi-criteria decision structure with an accuracy of 56.03%. A complementary study for our case study allowed us to explore how elements of a data-based model can improve a knowledge-based model, improving its accuracy by 7.07%. and showed that knowledge-based modeling can be an alternative to data-based modeling when the available dataset has approximately 60 instances. Data-based models tend to have better performance, but their replicability is conditioned by the diversity in the dataset used. Knowledge-based models may be simpler but allow expert supervision, and these models are not usually tied to specific sites.