2023
Report
Unknown
Artificial intelligence for image classification of wheat's phytopathologies, pests and infestants
Bruno A., Martinelli M., Moroni D., Rocchi L., Morelli S., Ferrari E., Toscano P., Dainelli R.
Efficientnet Wheat Disease Classification Deep learning Ensemble
In this work we present a solution to deal with issues related to wheat cultures: diseases, abiotic damages, weeds and pests. The core of this work is the EfficientNet architecture, in particular the b0 version which is the one with best accuracy/complexity ratio in our opinion. Results show that in this way accuracies are improved by +2% up to +10% while the complexity is unchanged. The models presented and tested during this work have been deployed and can be tested by using the mobile app Granoscan.
Source: ISTI Working papers, 2023
Back to previous page
BibTeX entry
@techreport{oai:it.cnr:prodotti:478391,
title = {Artificial intelligence for image classification of wheat's phytopathologies, pests and infestants},
author = {Bruno A. and Martinelli M. and Moroni D. and Rocchi L. and Morelli S. and Ferrari E. and Toscano P. and Dainelli R.},
institution = {ISTI Working papers, 2023},
year = {2023}
}
0000-0002-9152-8112CNR ExploRA