First / Co-first Authorship († Equal Contribution)
Tang, Z., Zhang, Z., (2026) Ladder-a software to label images, detect objects and deploy models recurrently for object detection: with a case to detect broken rice, (under review).
Tang, Z., Wang, J., Castro, A., Zhang, Y., Primo, V., Bernardino, A., Morota, G., Wang, X., Chebel, R., Yu, H.(2026). Can 3D point cloud data improve automated body condition score prediction in dairy cattle? (under review).
Weihs, B.†, Tang, Z†, Roy, S.†, et al. (2025) No More Laborious Stem Counting: AI-powered Computer Vision Enables Identification and Quantification of Solid and Hollow Alfalfa Stems at the Pixel Level, Smart Agriculture Technology.
Weihs, B.†, Tang, Z.†, et al. (2024) Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18, Plant Phenomics.
Tang, Z., et al. (2023) Affordable High Throughput Field Detection of Wheat Stripe Rust Using Deep Learning with Semi-Automated Image Labeling, Computers and Electronics in Agriculture.
Tang, Z., Hu, Y., and Zhang, Z. (2023) ROOSTER: An image labeler and classifier through interactive recurrent annotation. F1000Research.
Tang, Z., et al. (2021) Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation, Scientific Reports.
Co-Authorship
Tibbs, L., Han, L., Benke, R., Singh, P., Huang, H., Jewell, J., Trieu, T., Tang, Z., et al. (2026) Differential phenotypic plasticity mediates haplotype range and adaptation, (under review).
Bretas, I., et al. (2026) Estimating the botanical composition in grass-legume mixed pastures using aerial multispectral imagery and deep learning, (under review).
Zhou, W., Ouyang, H., Yan, Z., Song, J., Li, Y., Tang, Z., et al., Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study. The Plant Genome.
Song, J.Y., et al. (2025) Mapping invasive Opuntia stricta in Laikipia, Kenya using Sentinel-2 time series imagery and explainable machine learning. International Journal of Applied Earth Observation and Geoinformation.
Bretas, I., Dubeux,J, et al. (2025) Pedotransfer function for predicting deep-soil bulk density and assessing soil organic carbon and nitrogen stocks across land uses in coarse-textured soils, CATENA.
Weihs, B., Heuschele, D., Tang, Z., et al. (2024) The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review, Plant Phenomics.
J.C.B. Dubeux Jr, C. Zhao, L. Garcia, I. Brêtas, L. Queiroz, C. Erazo, J. Harley, A. Zare, Tang, Z. (2024) Soil organic carbon stocks in Florida grazing lands, The Florida Cattleman and Livestock Journal.
Liu, G., Jiang, X., He, C., Tang, Z. (2013) Neurexophilin 1 gene polymorphisms of chicken and its variation among species, BIOCHEMICAL GENETICS.
Preprint
1. Tang, Z., Wang, J., Castro, A., Zhang, Y., Primo, V., Bernardino, A., Morota, G., Wang, X., Chebel, R., Yu, H.(2026). Can 3D point cloud data improve automated body condition score prediction in dairy cattle? arXiv:2601.22522.
2. Tang, Z., and Zhang, Z. (2023). Ladder: A software to label images, detect objects and deploy models recurrently for object detection, arXiv:2306.10372.
3. Tovar, J. C., Tang, Z., Kinser, J. D., & Morgan, P. B. (2022). Detecting soil compaction with a ground penetrating radar. Authorea October 17, 2022.