2023
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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: COMPUTERS AND ELECTRONICS IN AGRICULTURE
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BibTeX entry
@misc{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},
year = {2023}
}
0000-0001-7419-5099