Video-Based Fruit Detection and Tracking for Apple Counting and Mapping

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dc.contributor.authorGené Mola, Jordi
dc.contributor.authorFelip Pomés, Marc
dc.contributor.authorNet Barnés, Francesc
dc.contributor.authorMorros Rubió, Josep Ramon
dc.contributor.authorMiranda, Juan Carlos
dc.contributor.authorArnó Satorra, Jaume
dc.contributor.authorAsin Jones, Luis
dc.contributor.authorLordan Sanahuja, Jaume
dc.contributor.authorRuiz Hidalgo, Javier
dc.contributor.authorGregorio López, Eduard
dc.date.accessioned2024-03-18T08:19:19Z
dc.date.available2024-03-18T08:19:19Z
dc.date.issued2023
dc.description.abstractAutomatic fruit counting systems have garnered interest from farmers and agronomists to monitor fruit production, predict yields in advance, and identify production variability across orchards. However, accurately counting fruits poses challenges, particularly due to occlusions. This study proposes a multi-view sensing approach using continuous motion videos captured by a camera moved along the row of trees, followed by fruit detection in all video frames and application of Multi-Object Tracking (MOT) algorithms to prevent double-counting. Three tracking methods, namely SORT, DeepSORT, and ByteTrack, are compared for fruit counting using the YOLOv5x object detector. The methodology is applied to map fruit production in an experimental apple orchard at two different dates: four weeks and one week before harvest. The results demonstrate that ByteTrack (MOTA=0.682; IDF1=0.837; HOTA=0.689) outperforms SORT and DeepSORT, indicating its superior tracking performance. Computational efficiency analysis reveals similar processing times between SORT and ByteTrack (about 15 ms), while DeepSORT requires significantly more processing time per image (128 ms). Fruit counting evaluation shows reasonably accurate yield predictions on both dates, with reduced errors and improved performance closer to the harvest date (MAPE=7.47 %; R2=0.70). The system proves effective in estimating orchard fruit production using computer vision technology, offering valuable insights for yield forecasting. These findings contribute to optimizing fruit production and supporting precision agriculture practices. The code and the dataset have been made publicly available and a video visualization of results is accessible at http://www.grap.udl.cat/en/publications/video_fruit_counting.
dc.description.sponsorshipThis work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU. The Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and European Social Fund (ESF) are also thanked for financing Juan Carlos Miranda’s pre doctoral fellowship (2020 FI_B 00586).
dc.identifier.doihttps://doi.org/10.1109/MetroAgriFor58484.2023.10424135
dc.identifier.urihttps://repositori.udl.cat/handle/10459.1/465318
dc.language.isoeng
dc.publisherIEEE
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094222-B-I00/ES/TECNOLOGIAS DE AGRICULTURA DE PRECISION PARA OPTIMIZAR EL MANEJO DEL DOSEL FOLIAR Y LA PROTECCION FITOSANITARIA SOSTENIBLE EN PLANTACIONES FRUTALES/
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-126648OB-I00/ES/PROTECCION DE CULTIVOS DE PRECISION PARA CONSEGUIR OBJETIVOS DEL PACTO VERDE EUROPEO EN USO EFICIENTE Y REDUCCION DE FITOSANITARIOS MEDIANTE AGRICULTURA DE PRECISION/
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117142GB-I00/ES/APRENDIZAJE PROFUNDO EFICIENTE PARA SECUENCIAS DE VIDEO Y NUBES DE PUNTOS/
dc.relation.isformatofVersió postprint del document publicat a https://doi.org/10.1109/MetroAgriFor58484.2023.10424135
dc.relation.ispartofJ. Gené-Mola et al., "Video-Based Fruit Detection and Tracking for Apple Counting and Mapping," 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Pisa, Italy, 2023, pp. 301-306, doi: 10.1109/MetroAgriFor58484.2023.10424135.
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectYield prediction
dc.subjectYield mapping
dc.subjectFruit Tracking
dc.subjectMulti-Object-Tracking
dc.subjectPrecision Agriculture
dc.titleVideo-Based Fruit Detection and Tracking for Apple Counting and Mapping
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
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