Research graph
References from Explainable spectral–image fusion multi-task learning for maize canopy biochemical and structural trait retrieval. Local targets link to admitted publications; unresolved targets remain external evidence.
Field high-throughput phenotyping: the new crop breeding frontier
10.1016/j.tplants.2013.09.008 · 2014 · External reference
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Multitask learning
10.1023/a:1007379606734 · 1997 · External reference
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10.1016/0034-4257(89)90069-2 · 1989 · External reference
10.1007/978-3-030-90673-3_4
10.1007/978-3-030-90673-3_4 · External reference
Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art
10.1109/mgrs.2017.2762087 · 2018 · External reference
Remote estimation of canopy chlorophyll content in crops
10.1029/2005gl022688 · 2005 · External reference
Optical properties and nondestructive estimation of anthocyanin content in plant leaves
10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2 · 2010 · External reference
Remote estimation of leaf area index and green leaf biomass in maize canopies
10.1029/2002gl016450 · 2003 · External reference
Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture
10.1016/j.rse.2003.12.013 · 2004 · External reference
Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture
10.1016/s0034-4257(02)00018-4 · 2002 · External reference
Spatial-spectral transformer for hyperspectral image classification
2021 · External reference
10.1109/tgrs.2021.3130716
10.1109/tgrs.2021.3130716 · External reference
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10.1111/j.1749-8198.2008.00182.x
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Significant remote sensing vegetation indices: a review of developments and applications
2017 · External reference
Deep learning in agriculture: a survey
10.1016/j.compag.2018.02.016 · 2018 · External reference
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
2018 · External reference
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External reference
Unresolved reference
External reference
10.1016/j.rse.2024.114547
10.1016/j.rse.2024.114547 · External reference
UAV multisensory data fusion and multi-task deep learning for high-throughput maize phenotyping
2023 · External reference
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External reference
Explaining Deep Neural Networks and beyond: a Review of Methods and applications
2021 · External reference
Challenges and opportunities in machine-augmented plant stress phenotyping
2020 · External reference
High-throughput phenotyping: breaking through the bottleneck in future crop breeding
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Nitrogen nutritional diagnosis of summer maize (Zea mays L.) based on a hyperspectral data collaborative approach-evaluation of the estimation potential of three-dimensional spectral indices
2025 · External reference
Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments
2024 · External reference
Red and photographic infrared linear combinations for monitoring vegetation
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10.1109/tpami.2021.3054719 · External reference
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10.1016/j.molp.2020.01.008 · External reference
A review of imaging techniques for plant phenotyping
10.3390/s141120078 · 2014 · External reference
A survey on multi-task learning
10.1109/jproc.2020.3004555 · 2021 · External reference
10.1007/978-3-030-90673-3_4
10.1007/978-3-030-90673-3_4 · ExternalCitation · doi-reference
Red and photographic infrared linear combinations for monitoring vegetation
10.1016/0034-4257(79)90013-0 · ExternalCitation · doi-reference
Remote sensing of foliar chemistry
10.1016/0034-4257(89)90069-2 · ExternalCitation · doi-reference
High-throughput phenotyping: breaking through the bottleneck in future crop breeding
10.1016/j.cj.2021.03.015 · ExternalCitation · doi-reference
Deep learning in agriculture: a survey
10.1016/j.compag.2018.02.016 · ExternalCitation · doi-reference
10.1016/j.molp.2020.01.008
10.1016/j.molp.2020.01.008 · ExternalCitation · doi-reference
Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture
10.1016/j.rse.2003.12.013 · ExternalCitation · doi-reference
10.1016/j.rse.2024.114547
10.1016/j.rse.2024.114547 · ExternalCitation · doi-reference
Field high-throughput phenotyping: the new crop breeding frontier
10.1016/j.tplants.2013.09.008 · ExternalCitation · doi-reference
Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture
10.1016/s0034-4257(02)00018-4 · ExternalCitation · doi-reference
Multitask learning
10.1023/a:1007379606734 · ExternalCitation · doi-reference
Remote estimation of leaf area index and green leaf biomass in maize canopies
10.1029/2002gl016450 · ExternalCitation · doi-reference
Remote estimation of canopy chlorophyll content in crops
10.1029/2005gl022688 · ExternalCitation · doi-reference
A survey on multi-task learning
10.1109/jproc.2020.3004555 · ExternalCitation · doi-reference
Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art
10.1109/mgrs.2017.2762087 · ExternalCitation · doi-reference
10.1109/tgrs.2021.3130716
10.1109/tgrs.2021.3130716 · ExternalCitation · doi-reference
10.1109/tpami.2021.3054719
10.1109/tpami.2021.3054719 · ExternalCitation · doi-reference
10.1111/j.1749-8198.2008.00182.x
10.1111/j.1749-8198.2008.00182.x · ExternalCitation · doi-reference
Optical properties and nondestructive estimation of anthocyanin content in plant leaves
10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2 · ExternalCitation · doi-reference
A review of imaging techniques for plant phenotyping
10.3390/s141120078 · ExternalCitation · doi-reference
