GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs
| Autor: | Luca Coviello, Marco Cristoforetti, Giuseppe Jurman, Cesare Furlanello |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 10, Iss 14, p 4870 (2020) |
| Druh dokumentu: | article |
| ISSN: | 10144870 2076-3417 |
| DOI: | 10.3390/app10144870 |
| Popis: | We introduce here the Grape Berries Counting Net (GBCNet), a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting. We test GBCNet using cross-validation procedure on two original datasets CR1 and CR2 of grape pictures taken in-field before veraison. A total of 35,668 berries have been manually annotated for the task. GBCNet achieves good performances on both the seven grape varieties dataset CR1, although with a different accuracy level depending on the variety, and on the single variety dataset CR2: in particular Mean Average Error (MAE) ranges from 0.85% for Pinot Gris to 11.73% for Marzemino on CR1 and reaches 7.24% on the Teroldego CR2 dataset. |
| Databáze: | Directory of Open Access Journals |
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