Crop stem detection and tracking for precision hoeing using deep learning
| dc.rights.license | open | en_US |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | LAC, Louis | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | DA COSTA, Jean-Pierre
ORCID: 0000-0003-2390-0047 IDREF: 060561459 | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | DONIAS, Marc | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | KERESZTES, Barna
IDREF: 143373161 | |
| dc.contributor.author | BARDET, Alain | |
| dc.date.accessioned | 2022-07-13T08:22:36Z | |
| dc.date.available | 2022-07-13T08:22:36Z | |
| dc.date.issued | 2022-01 | |
| dc.identifier.issn | 0168-1699 | en_US |
| dc.identifier.uri | oai:crossref.org:10.1016/j.compag.2021.106606 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/140461 | |
| dc.description.abstractEn | Developing alternatives to the chemical weeding process usually carried out in vegetable crop farming is necessary in order to reach a more sustainable agriculture. However, a precise mechanical weeding requires specific sensors and advanced computer vision algorithms to process crop and weed discrimination in real-time. In this paper we propose an algorithm able to detect, locate, and track the stem position of crops in images which is suitable for precision actions in vegetable fields such as mechanical hoeing within crop rows. The algorithm is twofold: (i) a deep neural network for object detection is first used to detect crop stems in individual RGB images and then (ii) an aggregation algorithm further refines the detections taking advantage of the temporal redundancy in consecutive frames. We evaluated the pipeline on images of maize and bean crops at an early stage of development, acquired in field conditions with a camera embedded in an experimental mechanical weeding system. We reported F1-scores of respectively 94.74% and 93.82% with a location accuracy around 0.7 cm when compared with human annotation. Moreover, this pipeline can operate in real-time on an embedded computer consuming as little power as 30 W. | |
| dc.description.sponsorship | Bloc-outil et Imagerie de Précision pour le Binage Intra-rang Précoce - ANR-17-ROSE-0001 | en_US |
| dc.language.iso | EN | en_US |
| dc.source | crossref | |
| dc.subject.en | Precision agriculture | |
| dc.subject.en | Deep learning | |
| dc.subject.en | Neural network | |
| dc.subject.en | Object detection | |
| dc.subject.en | Tracking algorithm | |
| dc.title.en | Crop stem detection and tracking for precision hoeing using deep learning | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1016/j.compag.2021.106606 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | en_US |
| bordeaux.journal | Computers and Electronics in Agriculture | en_US |
| bordeaux.page | 106606 | en_US |
| bordeaux.volume | 192 | en_US |
| bordeaux.hal.laboratories | Laboratoire d’Intégration du Matériau au Système (IMS) - UMR 5218 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | dissemin | |
| hal.identifier | hal-03722088 | |
| hal.version | 1 | |
| hal.date.transferred | 2022-07-13T08:22:38Z | |
| hal.export | true | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computers%20and%20Electronics%20in%20Agriculture&rft.date=2022-01&rft.volume=192&rft.spage=106606&rft.epage=106606&rft.eissn=0168-1699&rft.issn=0168-1699&rft.au=LAC,%20Louis&DA%20COSTA,%20Jean-Pierre&DONIAS,%20Marc&KERESZTES,%20Barna&BARDET,%20Alain&rft.genre=article |
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