Use of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) for Disease Detection in Tomato Crops: A Systematic Mapping

Tomato is one of the most widely grown vegetables in Colombia, and therefore one of the crops that is most affected by diseases during its life cycle. Most farmers lack the necessary knowledge, which results in low accuracy in the identification and classification of these diseases. The purpose of this paper is to perform a systematic mapping of the process of early detection of diseases affecting tomato crops. In this sense, an exhaustive review is made of the processes that are currently being carried out regarding the use of ANN and CNN, in the detection and classification of diseases that affect the state of the crop. For the preparation of this study, a bibliographic review of 33 papers from the following databases was performed: Scopus, ScienceDirect, and IEEE Xplore. Finally, it was found that the use of CNNs and ANNs can help identify diseases with a high level of accuracy, allowing farmers to apply appropriate treatments to their tomato crops.