| 000 | 04878nab|a22007577a|4500 | ||
|---|---|---|---|
| 001 | 69408 | ||
| 003 | MX-TxCIM | ||
| 005 | 20251016154349.0 | ||
| 008 | 20259s2025|||||xxu||p|op||||00||0|eng|dd | ||
| 022 | _a2643-6515 | ||
| 024 | 8 | _ahttps://doi.org/10.1016/j.plaphe.2025.100084 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 0 |
_aZijian Wang _940324 |
|
| 245 | 1 | 4 | _aThe Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset |
| 260 |
_aUnited States of America : _bElsevier B.V., _c2025. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aComputer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 %. However, the precision for stems with 54 % was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies. | ||
| 546 | _aText in English | ||
| 591 | _aPerez-Olivera, I. : Not in IRS staff list but CIMMYT Affiliation | ||
| 597 |
_dEuropean Commission (EC) _dInternational Wheat Yield Partnership (IWYP) _dHeat and Drought Wheat Improvement Consortium (HeDWIC) _dAccelerating Genetic Gains in Maize and Wheat (AGG) _dModernización Sustentable de la Agricultura Tradicional (MasAgro) _dSecretarÃa de Agricultura y Desarrollo Rural (SADER) _dFoundation for Food & Agriculture Research (FFAR) _dSwiss National Science Foundation (SNSF) _dAgence Nationale de la Recherche (ANR) _dBiotechnology and Biological Sciences Research Council (BBSRC) _dAnalytics for the Australian Grains Industry (AAGI) _dFund for Scientific Research (FNRS) _dNatural Sciences and Engineering Research Council of Canada (NSERC) _dCanada First Research Excellence Fund (CFREF) _dJapan Science and Technology Agency (JST) |
||
| 650 | 7 |
_aWheat _2AGROVOC _91310 |
|
| 650 | 7 |
_aFields _2AGROVOC _97065 |
|
| 650 | 7 |
_aHigh-throughput phenotyping _2AGROVOC _930900 |
|
| 650 | 7 |
_aBreeding _2AGROVOC _91029 |
|
| 700 | 1 |
_aZenkl, R. _940325 |
|
| 700 | 1 |
_aGreche, L. _940326 |
|
| 700 |
_aDe Solan, B. _910072 |
||
| 700 | 1 |
_aSamatan, L.B. _940327 |
|
| 700 | 0 |
_aSafaa Ouahid _940328 |
|
| 700 | 1 |
_aVisioni, A. _940329 |
|
| 700 | 1 |
_aRobles-Zazueta, C.A. _8001710187 _gGlobal Wheat Program _923303 |
|
| 700 | 1 |
_aPinto Espinosa, F. _8I1707012 _gFormerly Global Wheat Program _94431 |
|
| 700 | 1 |
_aPerez-Olivera, I. _940330 |
|
| 700 | 1 |
_aReynolds, M.P. _gGlobal Wheat Program _8INT1511 _9831 |
|
| 700 | 0 |
_aChen Zhu _940383 |
|
| 700 |
_aShouyang Liu _910189 |
||
| 700 | 1 |
_aD'argaignon, M.P. _940331 |
|
| 700 | 1 |
_aLopez-Lozano, R. _913779 |
|
| 700 |
_aWeiss, M. _910250 |
||
| 700 | 1 |
_aMarzougui, A. _930257 |
|
| 700 | 1 |
_aRoth, L. _940332 |
|
| 700 | 1 |
_aDandrifosse, S. _924311 |
|
| 700 | 1 |
_aCarlier, A. _924312 |
|
| 700 | 1 |
_aDumont, B. _91582 |
|
| 700 | 1 |
_aMercatoris, B. _924313 |
|
| 700 | 1 |
_aFernandez, J. _940333 |
|
| 700 | 1 |
_aChapman, S. _9458 |
|
| 700 | 1 |
_aNajafian, K. _940334 |
|
| 700 | 1 |
_aStavness, I. _924318 |
|
| 700 | 0 |
_aHaozhou Wang _924315 |
|
| 700 | 0 |
_aWei Guo _98202 |
|
| 700 | 1 |
_aVirlet, N. _940335 |
|
| 700 | 1 |
_aHawkesford, M.J. _910302 |
|
| 700 | 0 |
_aZhi Chen _940336 |
|
| 700 | 1 |
_aDavid, E. _924303 |
|
| 700 | 1 |
_aGillet, J. _913780 |
|
| 700 | 0 |
_aKamran Irfan _940337 |
|
| 700 |
_aComar, A. _910054 |
||
| 700 | 1 |
_aHund, A. _921619 |
|
| 773 | 0 |
_tPlant Phenomics _gv. 7, no. 3, art. 100084 _dUnited States of America : Elsevier B.V., 2025. _x2643-6515 |
|
| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35926 |
|
| 942 |
_cJA _n0 _2ddc |
||
| 999 |
_c69408 _d69400 |
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