Title
Detection of phytophthora palmivora in cocoa fruit with deep learning
Date Issued
23 June 2021
Access level
metadata only access
Resource Type
journal article
Abstract
Currently there are many supervised learning methods and evaluation models for the detection of diseases in plants, this initial study presents a model for the detection of phytophthora palmivora on the cocoa pod, which has been developed with the ResNet18 model, reaching 83% prediction accuracy, using 1596 images in total for training and testing. In addition, with the same model, other images were trained to predict the image of cocoa compared to other fruits similar to cocoa, obtaining a prediction accuracy of 96%. Due to the good results obtained in this work, we believe that this tool can help cocoa farmers to identify phytophthora and to obtain healthy cocoa crops.
Scopus EID
2-s2.0-85117878345
ISBN
9789895465910
Source
Iberian Conference on Information Systems and Technologies, CISTI
Resource of which it is part
Iberian Conference on Information Systems and Technologies, CISTI
ISSN of the container
21660727
Sources of information: Directorio de Producción Científica Scopus