Name
Abstract | ||
|---|---|---|
Seeds image analysis has become essential to preserve biodiversity. This is why recognition and classification of plant species on the earth's planet is nowadays a great challenge. The paper focuses on this purpose by studying two plant seeds datasets to classify their families or species through deep learning techniques. SeedNet, a novel CNN has been proposed to face the depicted issue, and several state-of-the-art convolutional neural networks have been exploited for an exhaustive comparison of most adequate for the considered scenario. In detail, promising results in seed classification for both analysed datasets, reaching accuracy values of 95.65% for the first one and 97.47% for the second one, have been obtained. The retrieval problem with the deep learning approach was also addressed, achieving satisfying performances. We consider the obtained results for both the tasks as an excellent starting point to develop a complete seeds recognition, classification and retrieval system to offer impressive support in agriculture and botany fields. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compag.2021.106269 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
Keywords | DocType | Volume |
Agriculture, Deep learning, Machine learning, Image analysis, Seeds classification, Retrieval | Journal | 187 |
ISSN | Citations | PageRank |
0168-1699 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Loddo | 1 | 12 | 5.43 |
| Mauro Loddo | 2 | 0 | 0.34 |
| Cecilia Di Ruberto | 3 | 0 | 0.68 |