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
@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} }