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2021 Journal Article Detecting sorghum plant and head features from multispectral UAV imageryZhao, Yan, Zheng, Bangyou, Chapman, Scott C., Laws, Kenneth, George-Jaeggli, Barbara, Hammer, Graeme L., Jordan, David R. and Potgieter, Andries B. (2021). Detecting sorghum plant and head features from multispectral UAV imagery. Plant Phenomics, 2021 9874650, 9874650-14. doi: 10.34133/2021/9874650 |
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2021 Journal Article Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methodsDavid, Etienne, Serouart, Mario, Smith, Daniel, Madec, Simon, Velumani, Kaaviya, Liu, Shouyang, Wang, Xu, Pinto, Francisco, Shafiee, Shahameh, Tahir, Izzat S. A., Tsujimoto, Hisashi, Nasuda, Shuhei, Zheng, Bangyou, Kirchgessner, Norbert, Aasen, Helge, Hund, Andreas, Sadhegi-Tehran, Pouria, Nagasawa, Koichi, Ishikawa, Goro, Dandrifosse, Sébastien, Carlier, Alexis, Dumont, Benjamin, Mercatoris, Benoit, Evers, Byron, Kuroki, Ken, Wang, Haozhou, Ishii, Masanori, Badhon, Minhajul A., Pozniak, Curtis ... Guo, Wei (2021). Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods. Plant Phenomics, 2021 9846158, 1-9. doi: 10.34133/2021/9846158 |
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2021 Journal Article Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learningDas, Sumanta, Christopher, Jack, Apan, Armando, Choudhury, Malini Roy, Chapman, Scott, Menzies, Neal W. and Dang, Yash P. (2021). Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning. Agricultural and Forest Meteorology, 307 108477, 108477. doi: 10.1016/j.agrformet.2021.108477 |
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2021 Journal Article Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniquesRoy Choudhury, Malini, Das, Sumanta, Christopher, Jack, Apan, Armando, Chapman, Scott, Menzies, Neal W. and Dang, Yash P. (2021). Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniques. Remote Sensing, 13 (17) 3482, 3482. doi: 10.3390/rs13173482 |
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2021 Journal Article Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metricsChoudhury, Malini Roy, Mellor, Vincent, Das, Sumanta, Christopher, Jack, Apan, Armando, Menzies, Neal W., Chapman, Scott and Dang, Yash P. (2021). Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics. Agricultural Water Management, 255 107007, 1-16. doi: 10.1016/j.agwat.2021.107007 |
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2021 Journal Article UAV-thermal imaging: a technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils – a case review on wheatDas, Sumanta, Chapman, Scott, Christopher, Jack, Roy Choudhury, Malini, Menzies, Neal W., Apan, Armando and Dang, Yash P. (2021). UAV-thermal imaging: a technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils – a case review on wheat. Remote Sensing Applications: Society and Environment, 23 100583, 1-13. doi: 10.1016/j.rsase.2021.100583 |
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2021 Journal Article Comparison of modelling strategies to estimate phenotypic values from an unmanned aerial vehicle with spectral and temporal vegetation indexesHu, Pengcheng, Chapman, Scott C., Jin, Huidong, Guo, Yan and Zheng, Bangyou (2021). Comparison of modelling strategies to estimate phenotypic values from an unmanned aerial vehicle with spectral and temporal vegetation indexes. Remote Sensing, 13 (14) 2827, 1-19. doi: 10.3390/rs13142827 |
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2021 Journal Article Genotype specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate changeBustos-Korts, Daniela, Boer, Martin P., Chenu, Karine, Zheng, Bangyou, Chapman, Scott and van Eeuwijk, Fred (2021). Genotype specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change. In Silico Plants, 3 (2) diab018. doi: 10.1093/insilicoplants/diab018 |
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2021 Journal Article Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of cropsHu, Pengcheng, Chapman, Scott C. and Zheng, Bangyou (2021). Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops. Functional Plant Biology, 48 (8), 766-779. doi: 10.1071/FP20309 |
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2021 Journal Article Scaling up high-throughput phenotyping for abiotic stress selection in the fieldSmith, Daniel T., Potgieter, Andries B. and Chapman, Scott C. (2021). Scaling up high-throughput phenotyping for abiotic stress selection in the field. Theoretical and Applied Genetics, 134 (6), 1845-1866. doi: 10.1007/s00122-021-03864-5 |
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2021 Journal Article Evolution and application of digital technologies to predict crop type and crop phenology in agriculturePotgieter, A. B., Zhao, Yan, Zarco-Tejada, Pablo J, Chenu, Karine, Zhang, Yifan, Porker, Kenton, Biddulph, Ben, Dang, Yash P., Neale, Tim, Roosta, Fred and Chapman, Scott (2021). Evolution and application of digital technologies to predict crop type and crop phenology in agriculture. In Silico Plants, 3 (1) diab017, 1-23. doi: 10.1093/insilicoplants/diab017 |
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2021 Journal Article An analysis of simulated yield data for pepper shows how genotype × environment interaction in yield can be understood in terms of yield components and their QTLsRodrigues, Paulo C., Heuvelink, Ep, Marcelis, Leo F. M., Chapman, Scott C. and van Eeuwijk, Fred A. (2021). An analysis of simulated yield data for pepper shows how genotype × environment interaction in yield can be understood in terms of yield components and their QTLs. Crop Science, 61 (3), 1826-1842. doi: 10.1002/csc2.20476 |
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2021 Journal Article UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soilDas, Sumanta, Christopher, Jack, Apan, Armando, Roy Choudhury, Malini, Chapman, Scott, Menzies, Neal W. and Dang, Yash P. (2021). UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 221-237. doi: 10.1016/j.isprsjprs.2021.01.014 |
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2021 Journal Article Limiting transpiration rate in high evaporative demand conditions to improve Australian wheat productivityCollins, Brian, Chapman, Scott, Hammer, Graeme and Chenu, Karine (2021). Limiting transpiration rate in high evaporative demand conditions to improve Australian wheat productivity. in silico Plants, 3 (1) diab006, 1-16. doi: 10.1093/insilicoplants/diab006 |
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2021 Journal Article Integrating crop growth models with remote sensing for predicting biomass yield of sorghumYang, Kai-Wei, Chapman, Scott, Carpenter, Neal, Hammer, Graeme, McLean, Greg, Zheng, Bangyou, Chen, Yuhao, Delp, Edward, Masjedi, Ali, Crawford, Melba, Ebert, David, Habib, Ayman, Thompson, Addie, Weil, Clifford and Tuinstra, Mitchell R (2021). Integrating crop growth models with remote sensing for predicting biomass yield of sorghum. In Silico Plants, 3 (1) diab001, 1-19. doi: 10.1093/insilicoplants/diab001 |
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2021 Conference Publication Domain adaptation for plant organ detection with style transferJames, Chrisbin, Gu, Yanyang, Chapman, Scott, Guo, Wei, David, Etienne, Madec, Simon, Potgieter, Andries and Eriksson, Anders (2021). Domain adaptation for plant organ detection with style transfer. International Conference on Digital Image Computing - Techniques and Applications (DICTA), Online, 29 November - 1 December 2021. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA52665.2021.9647293 |
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2021 Journal Article Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functionsCooper, M., Powell, O., Voss-Fels, K. P., Messina, C. D., Gho, C., Podlich, D. W., Technow, F., Chapman, S. C., Beveridge, C. A., Ortiz-Barrientos, D. and Hammer, G. L. (2021). Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions. in silico Plants, 3 (1) diaa016, 1-21. doi: 10.1093/insilicoplants/diaa016 |
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2020 Conference Publication UAV-thermal imaging: A robust technology to evaluate in-field crop water stress and yield variation of wheat genotypesDas, Sumanta, Christopher, Jack, Apan, Armando, Roy Choudhury, Malini, Chapman, Scott, Menzies, Neal W. and Dang, Yash P. (2020). UAV-thermal imaging: A robust technology to evaluate in-field crop water stress and yield variation of wheat genotypes. IEEE India Geoscience and Remote Sensing Symposium (InGARSS), Ahmedabad, India, 1-4 December 2020. Piscataway, NJ, United States: IEEE. doi: 10.1109/ingarss48198.2020.9358955 |
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2020 Journal Article Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution rgb-labelled images to develop and benchmark wheat head detection methodsDavid, E., Madec, Shouyang, Sadeghi-Tehran, Pouria, Aasen, Helge, Zheng, B., Liu, Simon, Kirchgessner, Norbert, Ishikawa, Goro, Nagasawa, Koichi, Badhon, Minhajul A., Pozniak, Curtis, de Solan, Benoit, Hund, Andreas, Chapman, Scott C., Baret, Fred, Stavness, Ian and Guo, Wei (2020). Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution rgb-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics, 2020 3521852, 1-12. doi: 10.34133/2020/3521852 |
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2020 Journal Article Breeder friendly phenotypingReynolds, Matthew, Chapman, Scott, Crespo-Herrera, Leonardo, Molero, Gemma, Mondal, Suchismita, Pequeno, Diego N.L., Pinto, Francisco, Pinera-Chavez, Francisco J., Poland, Jesse, Rivera-Amado, Carolina, Saint Pierre, Carolina and Sukumaran, Sivakumar (2020). Breeder friendly phenotyping. Plant Science, 295 110396, 110396. doi: 10.1016/j.plantsci.2019.110396 |
