Publication
Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
| dc.contributor.author | Corceiro, Ana | |
| dc.contributor.author | Pereira, Nuno José Matos | |
| dc.contributor.author | Alibabaei, Khadijeh | |
| dc.contributor.author | Gaspar, Pedro Dinis | |
| dc.date.accessioned | 2024-01-22T16:10:20Z | |
| dc.date.available | 2024-01-22T16:10:20Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/a17010019 | pt_PT |
| dc.identifier.issn | 1999-4893 | |
| dc.identifier.uri | http://hdl.handle.net/10400.6/14100 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Algorithms | pt_PT |
| dc.relation.publisherversion | Algorithms | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Agriculture | pt_PT |
| dc.subject | CNN | pt_PT |
| dc.subject | ML algorithms | pt_PT |
| dc.subject | Flora classification | pt_PT |
| dc.subject | Precision agriculture | pt_PT |
| dc.title | Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| person.familyName | Corceiro | |
| person.familyName | Alibabaei | |
| person.familyName | Gaspar | |
| person.givenName | Ana | |
| person.givenName | Khadijeh | |
| person.givenName | Pedro Dinis | |
| person.identifier | 2073838 | |
| person.identifier.ciencia-id | 3816-4976-98BF | |
| person.identifier.ciencia-id | 6111-9F05-2916 | |
| person.identifier.orcid | 0000-0002-0502-7953 | |
| person.identifier.orcid | 0000-0002-2319-8211 | |
| person.identifier.orcid | 0000-0003-1691-1709 | |
| person.identifier.rid | N-3016-2013 | |
| person.identifier.scopus-author-id | 57419570900 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 9863752d-5e00-470a-afc3-f51f8a459fe8 | |
| relation.isAuthorOfPublication | 42cdea19-a0b8-4e67-89cd-405635dede48 | |
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| relation.isAuthorOfPublication.latestForDiscovery | 42cdea19-a0b8-4e67-89cd-405635dede48 |