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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

Distinguishing knowledge mapping from biophysical evidence: A Rejoinder to "Comment on 'Mapping Soil Organic Carbon Research in Conservation Agriculture'"

2026

Journal Article

Mapping soil organic carbon research in conservation agriculture: a systematic review

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). 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

Mapping soil organic carbon research in conservation agriculture: a systematic review

2026

Journal Article

StomataSeg: Semi-supervised instance segmentation for sorghum stomatal components

Huang, 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

StomataSeg: Semi-supervised instance segmentation for sorghum stomatal components

2026

Journal Article

Reduced-rank random regression for improved yield prediction across the Australian wheatbelt

Ramakers, 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

Reduced-rank random regression for improved yield prediction across the Australian wheatbelt

2026

Journal Article

A novel physics-based empirical line approach for reflectance calibration of UAV multispectral imagery

Ashourloo, 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

A novel physics-based empirical line approach for reflectance calibration of UAV multispectral imagery

2026

Other Outputs

Environmental covariate data, envirotype classifications, and analysis code for wheat G×E and trialling efficiency in the Australian wheatbelt

Chapman, Scott, Arief, Vivi, Gho Brito, Carla and Fernandez, Javier (2026). Environmental covariate data, envirotype classifications, and analysis code for wheat G×E and trialling efficiency in the Australian wheatbelt. The University of Queensland. (Dataset) doi: 10.48610/b4394a5

Environmental covariate data, envirotype classifications, and analysis code for wheat G×E and trialling efficiency in the Australian wheatbelt

2026

Journal Article

From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series

Devoto, 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

From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series

2026

Journal Article

Envirotypes for wheat G×E in Australia: integrating multiple environmental indices to characterise variety trials

Fernandez, 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

Envirotypes for wheat G×E in Australia: integrating multiple environmental indices to characterise variety trials

2026

Journal Article

Beyond carbon measurement: turning agricultural carbon into credible climate action

Li, 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

Beyond carbon measurement: turning agricultural carbon into credible climate action

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

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]

2026

Journal Article

CropVision: A Multiscale EO-AI-Climate-Biophysical modelling framework for predicting crop performance in Australian wheat and sorghum systems

Potgieter, 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

CropVision: A Multiscale EO-AI-Climate-Biophysical modelling framework for predicting crop performance in Australian wheat and sorghum systems

2026

Journal Article

Synthesizing crop modelling and deep learning for remote estimation of wheat biomass dynamics from multispectral and weather observations

Chen, 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

Synthesizing crop modelling and deep learning for remote estimation of wheat biomass dynamics from multispectral and weather observations

2026

Journal Article

Versioned soil organic carbon maps for climate-resilient and sustainable cities: a ridge-penalised linear mixed-effects ensemble of MIR-based predictions

Tian, 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

Versioned soil organic carbon maps for climate-resilient and sustainable cities: a ridge-penalised linear mixed-effects ensemble of MIR-based predictions

2026

Other Outputs

Sorghum diversity head counting dataset

Karannagoda, Rukshan, Baktashmotlagh, Mahsa, Chapman, Scott, Wang, Zijian, Guo, Kaiyu, Luo, Yadan, James, Chrisbin and Zheng, Bangyou (2026). Sorghum diversity head counting dataset. The University of Queensland. (Dataset) doi: 10.48610/45beb77

Sorghum diversity head counting dataset

2026

Journal Article

Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: effects of sample size, dimensionality, and transferability

Ashourloo, 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

Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: effects of sample size, dimensionality, and transferability

2026

Journal Article

A large-scale in-the-wild dataset for plant disease segmentation

Wei, 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

A large-scale in-the-wild dataset for plant disease segmentation

2026

Conference Publication

Augment to segment: tackling pixel-level imbalance in wheat disease and pest segmentation

Wei, Tianqi, Yu, Xin, Chen, Zhi, Chapman, Scott and Huang, Zi (2026). Augment to segment: tackling pixel-level imbalance in wheat disease and pest segmentation. 36th Australasian Database Conference, Sydney, NSW Australia and Bali, Indonesia, 4-6 December 2025. Singapore: Springer Singapore. doi: 10.1007/978-981-95-6196-4_3

Augment to segment: tackling pixel-level imbalance in wheat disease and pest segmentation

2026

Conference Publication

Dynamic orchestration of multi-agent system for real-world multi-image agricultural VQA

Ke, Yan, Yu, Xin, Du, Heming, Chapman, Scott and Huang, Helen (2026). Dynamic orchestration of multi-agent system for real-world multi-image agricultural VQA. 36th Australasian Database Conference, Sydney, NSW Australia and Bali, Indonesia, 4-6 December 2025. Singapore: Springer Singapore. doi: 10.1007/978-981-95-6196-4_11

Dynamic orchestration of multi-agent system for real-world multi-image agricultural VQA

2026

Book Chapter

Benchmarking Vision-Language Models for Microscopic Plant Image Understanding

Wei, Tianqi, Yu, Xin, Chen, Zhi, Chapman, Scott and Huang, Zi (2026). Benchmarking Vision-Language Models for Microscopic Plant Image Understanding. Lecture Notes in Computer Science. (pp. 239-258) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-032-37432-5_14

Benchmarking Vision-Language Models for Microscopic Plant Image Understanding

2026

Journal Article

Sensor-based estimation of evapotranspiration and water use efficiency in Australian wheat variety trials

Fernandez, 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

Sensor-based estimation of evapotranspiration and water use efficiency in Australian wheat variety trials