Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation
Herbicide research and development necessitate specific trials to monitor the effects of various herbicide formulations, quantities, and protocols on different plant species and growth stages. These trials are necessary to ensure the safety and efficacy of the developed products. Currently, these te...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003654 |
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| author | Artzai Picon Daniel Mugica Itziar Eguskiza Arantza Bereciartua-Perez Javier Romero Carlos Javier Jimenez Christian Klukas Laura Gomez-Zamanillo Till Eggers Ramon Navarra-Mestre |
| author_facet | Artzai Picon Daniel Mugica Itziar Eguskiza Arantza Bereciartua-Perez Javier Romero Carlos Javier Jimenez Christian Klukas Laura Gomez-Zamanillo Till Eggers Ramon Navarra-Mestre |
| author_sort | Artzai Picon |
| collection | DOAJ |
| description | Herbicide research and development necessitate specific trials to monitor the effects of various herbicide formulations, quantities, and protocols on different plant species and growth stages. These trials are necessary to ensure the safety and efficacy of the developed products. Currently, these tests are conducted manually and assessed visually, making the process time-consuming and labor-intensive. Developing a computer model to characterize species, damage, and growth stages is challenging due to the fine-grained differences between species and damage, significant intra-class variability, and difficulties in manual annotations. Additionally, manually annotated datasets for semantic segmentation are often imperfect. The presence of non-target or unknown species, where only the genus or family is known, complicates the management and scalability of these datasets.In this work, we propose a new hierarchical loss function, suitable for semantic segmentation tasks, capable to take advantage for the hierarchical taxonomy relationships between species, plant damages and other relationships and thus, reduce the need for annotated data. The proposed loss function support datasets with varying granularity and annotation heterogeneity, including for partial annotations at the pixel level. We validated this loss function using a multi-task semantic segmentation neural network to simultaneously detect plant species and quantify the damage of each species. The proposed hierarchical loss function improves model performance, increasing the F1-Score for species detection from 0.41 to 0.52, for damage detection from 0.23 to 0.28. This enhancement forces the model to learn richer hierarchical representations, enabling the support of heterogeneous and partially annotated scalable datasets, which are common in real-world AI applications. |
| format | Article |
| id | doaj-art-36192e6411474c659eb0deaeb7f7b660 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-36192e6411474c659eb0deaeb7f7b6602025-08-20T02:55:45ZengElsevierSmart Agricultural Technology2772-37552025-03-011010076110.1016/j.atech.2024.100761Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentationArtzai Picon0Daniel Mugica1Itziar Eguskiza2Arantza Bereciartua-Perez3Javier Romero4Carlos Javier Jimenez5Christian Klukas6Laura Gomez-Zamanillo7Till Eggers8Ramon Navarra-Mestre9TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio - Bizkaia, Spain; University of the Basque Country, Plaza Torres Quevedo, 48013 Bilbao, Spain; Corresponding author at: TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio - Bizkaia, Spain.TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio - Bizkaia, SpainTECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio - Bizkaia, SpainTECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio - Bizkaia, SpainBASF Espanola S.L. Carretera A376, 41710 Utrera, Sevilla, SpainBASF Espanola S.L. Carretera A376, 41710 Utrera, Sevilla, SpainBASF SE, Speyererstrasse 2, 67117 Limburgerhof, GermanyTECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio - Bizkaia, SpainBASF SE, Speyererstrasse 2, 67117 Limburgerhof, GermanyBASF SE, Speyererstrasse 2, 67117 Limburgerhof, GermanyHerbicide research and development necessitate specific trials to monitor the effects of various herbicide formulations, quantities, and protocols on different plant species and growth stages. These trials are necessary to ensure the safety and efficacy of the developed products. Currently, these tests are conducted manually and assessed visually, making the process time-consuming and labor-intensive. Developing a computer model to characterize species, damage, and growth stages is challenging due to the fine-grained differences between species and damage, significant intra-class variability, and difficulties in manual annotations. Additionally, manually annotated datasets for semantic segmentation are often imperfect. The presence of non-target or unknown species, where only the genus or family is known, complicates the management and scalability of these datasets.In this work, we propose a new hierarchical loss function, suitable for semantic segmentation tasks, capable to take advantage for the hierarchical taxonomy relationships between species, plant damages and other relationships and thus, reduce the need for annotated data. The proposed loss function support datasets with varying granularity and annotation heterogeneity, including for partial annotations at the pixel level. We validated this loss function using a multi-task semantic segmentation neural network to simultaneously detect plant species and quantify the damage of each species. The proposed hierarchical loss function improves model performance, increasing the F1-Score for species detection from 0.41 to 0.52, for damage detection from 0.23 to 0.28. This enhancement forces the model to learn richer hierarchical representations, enabling the support of heterogeneous and partially annotated scalable datasets, which are common in real-world AI applications.http://www.sciencedirect.com/science/article/pii/S2772375524003654Taxonomic hierarchical lossPlant species and damage segmentationPrecision agriculturePrecise phenotypingDeep learning |
| spellingShingle | Artzai Picon Daniel Mugica Itziar Eguskiza Arantza Bereciartua-Perez Javier Romero Carlos Javier Jimenez Christian Klukas Laura Gomez-Zamanillo Till Eggers Ramon Navarra-Mestre Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation Smart Agricultural Technology Taxonomic hierarchical loss Plant species and damage segmentation Precision agriculture Precise phenotyping Deep learning |
| title | Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation |
| title_full | Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation |
| title_fullStr | Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation |
| title_full_unstemmed | Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation |
| title_short | Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation |
| title_sort | taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi task semantic segmentation |
| topic | Taxonomic hierarchical loss Plant species and damage segmentation Precision agriculture Precise phenotyping Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003654 |
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