Considering the population growth rate of recent years, a doubling of the current worldwide
crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to
achieving this productivity outcome. Therefore, it is very important to develop efficient methods
for the automatic detection, identification, and prediction of pests and diseases in agricultural crops.
To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge
and relationships from the data that is being worked on. This paper presents a literature review on
ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and
prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute
to the development of smart farming and precision agriculture by promoting the development of
techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving
and improving their crop quality and production.info:eu-repo/semantics/publishedVersio
