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2026 Journal Article StomataSeg: Semi-supervised instance segmentation for sorghum stomatal componentsHuang, Zhongtian, Chen, Zhi, Huang, Zi, Yu, Xin, Smith, Daniel, Purushothama, Chaitanya, Van Oosterom, Erik, Wu, Alex, Salter, William, Li, Yan and Chapman, Scott (2026). StomataSeg: Semi-supervised instance segmentation for sorghum stomatal components. Computers and Electronics in Agriculture 112442, 112442-112442. doi: 10.1016/j.compag.2026.112442 |
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2026 Journal Article Mapping soil organic carbon research in conservation agriculture: a systematic reviewLiu, Qingyang, Tian, Xin, Dalal, Ram C., Wu, Jinran, Xia, Anquan, Wu, Xiaoxuan, Li, Tong, McLachlan, Geoffrey J., Chapman, Scott and Dang, Yash P. (2026). Mapping soil organic carbon research in conservation agriculture: a systematic review. Farming System, 4 (4) 100241, 1-11. doi: 10.1016/j.farsys.2026.100241 |
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2026 Journal Article Distinguishing knowledge mapping from biophysical evidence: A Rejoinder to "Comment on 'Mapping Soil Organic Carbon Research in Conservation Agriculture'"Liu, Qingyang, Tian, Xin, Dalal, Ram C., Wu, Jinran, Xia, Anquan, Wu, Xiaoxuan, Li, Tong, McLachlan, Geoffrey J., Chapman, Scott and Dang, Yash P. (2026). Distinguishing knowledge mapping from biophysical evidence: A Rejoinder to "Comment on 'Mapping Soil Organic Carbon Research in Conservation Agriculture'". Farming System, 4 (4) 100255, 1-4. doi: 10.1016/j.farsys.2026.100255 |
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2026 Journal Article A novel physics-based empirical line approach for reflectance calibration of UAV multispectral imageryAshourloo, Davoud, Smith, Daniel, Cave, Lleyton, James, Chris, Chen, Xiaolong, Marcano, Sebastian Lopez, Oya, Riku, Gho Brito, Carla, Potgieter, Andries and Chapman, Scott (2026). A novel physics-based empirical line approach for reflectance calibration of UAV multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 64, 1-16. doi: 10.1109/tgrs.2026.3737691 |
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2026 Journal Article Reduced-rank random regression for improved yield prediction across the Australian wheatbeltRamakers, Jip J. C., Boer, Martin P., Hemerik, Jesse, Bustos-Korts, Daniela, Gezan, Salvador A., Arief, Vivi, Fernandez, Javier A., Chapman, Scott C. and van Eeuwijk, Fred A. (2026). Reduced-rank random regression for improved yield prediction across the Australian wheatbelt. G3: Genes, Genomes, Genetics jkag272. doi: 10.1093/g3journal/jkag272 |
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2026 Journal Article From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time seriesDevoto, Francesca, Bange, Michael, Camino, Carlos, Woodgate, William, Chapman, Scott and Potgieter, Andries (2026). From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series. Plant Phenomics, 8 (3) 100220, 1-18. doi: 10.1016/j.plaphe.2026.100220 |
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2026 Journal Article Envirotypes for wheat G×E in Australia: integrating multiple environmental indices to characterise variety trialsFernandez, Javier A, Hu, Pengcheng, Arief, Vivi, Gho, Carla, Zheng, Bangyou, Ramakers, Jip, Boer, Martin, van Eeuwijk, Fred and Chapman, Scott C (2026). Envirotypes for wheat G×E in Australia: Integrating multiple environmental indices to characterise variety trials. G3: Genes, Genomes, Genetics jkag222. doi: 10.1093/g3journal/jkag222 |
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2026 Journal Article Beyond carbon measurement: turning agricultural carbon into credible climate actionLi, Tong, Tian, Xin, Dalal, Ram C., Kyriazopoulos, Apostolos, Sharififar, Amin, Xia, Anquan, Pandey, Rajiv, Fu, Peng, Bolan, Nanthi, Wu, Jinran, McLachlan, Geoffrey J., Choi, Woo-Jung, Xu, Zhihong, Wang, Yanfen, Fu, Bojie, Filipović, Vilim, Chapman, Scott, Dang, Yash P. and Camps-Valls, Gustau (2026). Beyond carbon measurement: turning agricultural carbon into credible climate action. The Innovation, 7 (9) 101531, 1-2. doi: 10.1016/j.xinn.2026.101531 |
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2026 Journal Article Corrigendum to “Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: Effects of sample size, dimensionality, and transferability” [Smart Agricultural Technology, Volume 13 (2026), Article 101936]Ashourloo, Davoud, Brider, Jason, Zhao, Yan, Jiang, Ruizhu, Zhang, Miao, Chapman, Scott, Hammer, Graeme and Potgieter, Andries B. (2026). Corrigendum to “Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: Effects of sample size, dimensionality, and transferability” [Smart Agricultural Technology, Volume 13 (2026), Article 101936]. Smart Agricultural Technology, 14 101983, 1-1. doi: 10.1016/j.atech.2026.101983 |
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2026 Journal Article CropVision: A Multiscale EO-AI-Climate-Biophysical modelling framework for predicting crop performance in Australian wheat and sorghum systemsPotgieter, Andries B., Brider, Jason, Ashourloo, Davoud, Schepen, Andrew, Zhao, Yan, Jiang, Ruizhu, Chapman, Scott, Zhang, Miao, Wu, Bingfang, Russell, Kerry and Hammer, Graeme (2026). CropVision: A Multiscale EO-AI-Climate-Biophysical modelling framework for predicting crop performance in Australian wheat and sorghum systems. in silico Plants, 8 (2) diag018, 1-18. doi: 10.1093/insilicoplants/diag018 |
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2026 Journal Article Versioned soil organic carbon maps for climate-resilient and sustainable cities: a ridge-penalised linear mixed-effects ensemble of MIR-based predictionsTian, Xin, Liu, Qingyang, Dalal, Ram C., Li, Tong, Wu, Jinran, McLachlan, Geoffrey J., Chapman, Scott and Dang, Yash P. (2026). Versioned soil organic carbon maps for climate-resilient and sustainable cities: a ridge-penalised linear mixed-effects ensemble of MIR-based predictions. Physics and Chemistry of the Earth, 143 104390, 1-15. doi: 10.1016/j.pce.2026.104390 |
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2026 Journal Article Synthesizing crop modelling and deep learning for remote estimation of wheat biomass dynamics from multispectral and weather observationsChen, Qiaomin, Chen, Zhi, Hu, Pengcheng, Zheng, Bangyou, Smith, Daniel T. L., Fernandez, Javier, Garba, Ismail I. and Chapman, Scott C. (2026). Synthesizing crop modelling and deep learning for remote estimation of wheat biomass dynamics from multispectral and weather observations. Artificial Intelligence in Agriculture. doi: 10.1016/j.aiia.2026.06.007 |
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2026 Journal Article Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: effects of sample size, dimensionality, and transferabilityAshourloo, Davoud, Brader, Jason, Zhao, Yan, Jiang, Ruizhu, Zhang, Miao, Chapman, Scott, Hammer, Graeme and Potgieter, Andries B (2026). Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: effects of sample size, dimensionality, and transferability. Smart Agricultural Technology, 13 101936, 1-13. doi: 10.1016/j.atech.2026.101936 |
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2026 Journal Article A large-scale in-the-wild dataset for plant disease segmentationWei, Tianqi, Chen, Zhi, Yu, Xin, Chapman, Scott, Melloy, Paul and Huang, Zi (2026). A large-scale in-the-wild dataset for plant disease segmentation. Scientific Data, 13 (1) 205, 1-15. doi: 10.1038/s41597-025-06513-4 |
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2026 Journal Article Sensor-based estimation of evapotranspiration and water use efficiency in Australian wheat variety trialsFernandez, Javier A., Gho, Carla, Smith, Daniel T., Hu, Pengcheng, Zheng, Bangyou and Chapman, Scott C. (2026). Sensor-based estimation of evapotranspiration and water use efficiency in Australian wheat variety trials. in silico Plants, 8 (1) diag009, 1-17. doi: 10.1093/insilicoplants/diag009 |
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2025 Journal Article Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbeanVan Haeften, Shanice, Smith, Daniel, Robinson, Hannah, Dudley, Caitlin, Kang, Yichen, Douglas, Colin A., Hickey, Lee T., Potgieter, Andries, Chapman, Scott and Smith, Millicent R. (2025). Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbean. The Plant Phenome Journal, 8 (1) e70016, 1-18. doi: 10.1002/ppj2.70016 |
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2025 Journal Article Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the fieldAlahmad, Samir, Smith, Daniel, Katsikis, Christina, Aldiss, Zachary, Brunner, Stephanie M., Meer, Sarah V., Meijer, Lotus, Heidariask, Bita, Chenu, Karine, Chapman, Scott, Potgieter, Andries B., Wasson, Anton, Baraibar, Silvina, Godoy, Jayfred, Moody, David, Robinson, Hannah and Hickey, Lee T. (2025). Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field. Journal of Experimental Botany, 76 (17) eraf268, 5161-5178. doi: 10.1093/jxb/eraf268 |
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2025 Journal Article The Global Wheat Full Semantic Organ Segmentation (GWFSS) datasetWang, Zijian, Zenkl, Radek, Greche, Latifa, De Solan, Benoit, Samatan, Lucas Bernigaud, Ouahid, Safaa, Visioni, Andrea, Robles-Zazueta, Carlos A., Pinto, Francisco, Perez-Olivera, Ivan, Reynolds, Matthew P., Zhu, Chen, Liu, Shouyang, D’argaignon, Marie-Pia, Lopez-Lozano, Raul, Weiss, Marie, Marzougui, Afef, Roth, Lukas, Dandrifosse, Sébastien, Carlier, Alexis, Dumont, Benjamin, Mercatoris, Benoît, Fernandez, Javier, Chapman, Scott, Najafian, Keyhan, Stavness, Ian, Wang, Haozhou, Guo, Wei, Virlet, Nicolas ... Hund, Andreas (2025). The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset. Plant Phenomics, 7 (3) 100084, 1-16. doi: 10.1016/j.plaphe.2025.100084 |
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2025 Journal Article Characterising wheat and barley growth and phenology using multispectral remote sensing for site-specific precision agricultureZhao, Yan, Jiang, Ruizhu, Brider, Jason, Chapman, Scott and Potgieter, Andries (2025). Characterising wheat and barley growth and phenology using multispectral remote sensing for site-specific precision agriculture. In Silico Plants, 7 (2) diaf013. doi: 10.1093/insilicoplants/diaf013 |
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2025 Journal Article Comprehensive assessment of PeriodiCT model for canopy temperature forecastingShao, Quanxi, Roche, Rose, Jamali, Hiz, Nunn, Chris, Zheng, Bangyou, Jin, Huidong, Chapman, Scott C. and Bange, Michael (2025). Comprehensive assessment of PeriodiCT model for canopy temperature forecasting. Agronomy, 15 (7) 1665, 1-30. doi: 10.3390/agronomy15071665 |
