In recent years, several proposals have been based on Artificial Intelligence
techniques for automatically detecting the presence of pests and diseases in
crops from images usually taken with a camera. By training with pictures of
affected crops and healthy crops, artificial intelligence techniques learn
to distinguish one from the other. Furthermore, in the long term, it is
intended that the tools developed from such approaches will allow the
automation and increased frequency of plant analysis, thus increasing the
possibility of determining and predicting crop health and potential biotic
risks. However, the great diversity of proposed solutions leads us to the
need to study them, present possible situations for their improvement, such
as image preprocessing, and analyse the robustness of the proposals examined
against more realistic pictures than those existing in the datasets
typically used. Taking all this into account, this paper embarks on a
comprehensive exploration of various AI techniques leveraging leaf images
for the autonomous detection of plant diseases. By fostering a deeper
understanding of the strengths and limitations of these methodologies, this
research contributes to the vanguard of agricultural disease detection,
propelling innovation, and fostering the maturation of AI-driven solutions
in this critical domain.