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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
29/07/2024 |
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Data da última atualização: |
29/07/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
TETILA, E. C.; MORO, B. L.; ASTOLFI, G.; COSTA, A. B. da; AMORIM, W. P.; BELETE, N. A. de S.; PISTORI, H.; BARBEDO, J. G. A. |
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Afiliação: |
EVERTON CASTELÃO TETILA, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS; BARBARA LOPES MORO, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS; GILBERTO ASTOLFI, INSTITUTO FEDERAL DE MATO GROSSO DO SUL; ANDERSON BESSA DA COSTA, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; WILLIAN PARAGUASSU AMORIM, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS; NÍCOLAS ALESSANDRO DE SOUZA BELETE, UNIVERSIDADE FEDERAL DE RONDÔNIA; HEMERSON PISTORI, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
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Título: |
Real-time detection of weeds by species in soybean using UAV images. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Crop Protection, v. 184, 106846, Oct. 2024. |
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DOI: |
https://doi.org/10.1016/j.cropro.2024.106846 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT. In this work, we evaluated a family of You Only Look Once (YOLOv5) object detection models for real-time detection of weeds in soybean fields. Based on the results generated by the detection, agricultural inputs can be applied to the identified regions of interest, lowering production costs by reducing the average number of sprayings and contributing to ecological balance and environmental preservation. We used the UAV to fly over three agricultural areas at an altitude of 10 m. Thus, we created a new dataset of 4129 annotated plant samples, which can serve as a baseline for weed detection in soybean crops. We considered four metrics to evaluate the classification results and three to evaluate detection results. Experimental results showed low average error rates in almost all test scenarios. YOLOv5s6 produced the best results among the evaluated models, obtaining MAE, RMSE, and R2 rates of 1.14, 1.67, and 0.93, respectively. We also demonstrate how the model can be deployed as part of an end-to-end system for herbicide application. |
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Palavras-Chave: |
Aprendizado profundo; Deep learning; Detecção de objeto; Object detection; Weed. |
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Thesagro: |
Agricultura de Precisão; Erva Daninha; Glycine Max; Soja. |
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Thesaurus Nal: |
Precision agriculture; Soybeans; Unmanned aerial vehicles. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02079naa a2200361 a 4500
001 2166007
005 2024-07-29
008 2024 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.cropro.2024.106846$2DOI
100 1 $aTETILA, E. C.
245 $aReal-time detection of weeds by species in soybean using UAV images.$h[electronic resource]
260 $c2024
520 $aABSTRACT. In this work, we evaluated a family of You Only Look Once (YOLOv5) object detection models for real-time detection of weeds in soybean fields. Based on the results generated by the detection, agricultural inputs can be applied to the identified regions of interest, lowering production costs by reducing the average number of sprayings and contributing to ecological balance and environmental preservation. We used the UAV to fly over three agricultural areas at an altitude of 10 m. Thus, we created a new dataset of 4129 annotated plant samples, which can serve as a baseline for weed detection in soybean crops. We considered four metrics to evaluate the classification results and three to evaluate detection results. Experimental results showed low average error rates in almost all test scenarios. YOLOv5s6 produced the best results among the evaluated models, obtaining MAE, RMSE, and R2 rates of 1.14, 1.67, and 0.93, respectively. We also demonstrate how the model can be deployed as part of an end-to-end system for herbicide application.
650 $aPrecision agriculture
650 $aSoybeans
650 $aUnmanned aerial vehicles
650 $aAgricultura de Precisão
650 $aErva Daninha
650 $aGlycine Max
650 $aSoja
653 $aAprendizado profundo
653 $aDeep learning
653 $aDetecção de objeto
653 $aObject detection
653 $aWeed
700 1 $aMORO, B. L.
700 1 $aASTOLFI, G.
700 1 $aCOSTA, A. B. da
700 1 $aAMORIM, W. P.
700 1 $aBELETE, N. A. de S.
700 1 $aPISTORI, H.
700 1 $aBARBEDO, J. G. A.
773 $tCrop Protection$gv. 184, 106846, Oct. 2024.
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