The quick diagnosis of diseases in plants that have economic value is essential to guarantee food security and avoid larger losses as a consequence of disease spreading. Two major problems may hinder this goal: 1) the permanent monitoring of all the plants by people capable of detecting diseases is mostly unviable; 2) in many cases, the person who detects the symptoms doesn't have enough knowledge to identify their causes. Despite the existance of solutions that explore technology as a facilitator to the process of quick diagnosis, which normally involves a referential database which might be consulted by the user, the emergence of systems which are really automated has been rather slow, especially considering the importance of the problem . This occurs not because of researchers' lack of interest, but because of the lack of a sufficiently broad database that could provide for the development of truly robust diagnosis methods. Thus, as important as the development of the technique itself is, so is the need to build a database which allows the complete validation of the proposed methods. Therefore, the present project has two main goals: 1) to build a database which contains images of symptoms and detailed descriptions of their causes and consequences; 2) develop methods capable of enabling a trustworthy diagnosis with the use of digital images supplied by the users. The methods will be implemented in two versions, one for mobile devices, and the other with a web interface. The crops to be initially considered will be cotton, soybeans, corn, rice, beans, wheat, sorghum, sugarcane, citrus fruits, vines, pineapple, coffee, cupuaçú, açaí, anthurium, melon, oil palm, coconut, and black pepper, although others might be added throughout the project.