Publications
Wiseman, M. S., Woods, J. L., Hartgrave, C. R., Richardson, B. J., & Gent, D. H. (2025). Automated detection and quantification of two-spotted spider mite life stages using computer vision for high-throughput in vitro assays. PloS one, 20(12).
Sapkota, S., Martinez, D., Underhill, A., Chen, L. L., Gadoury, D., Cadle-Davidson, L., & Hwang, C. F. (2025). A Device for Computer Vision Analysis of Fungal Features Outperforms Quantitative Manual Microscopy by Experts in Discerning a Host Resistance Locus. Phytopathology.
Gambhir, N., Paul, A., Qiu, T., Combs, D. B., Hosseinzadeh, S., Underhill, A., … & Gold, K. M. (2024). Non-destructive monitoring of foliar fungicide efficacy with hyperspectral sensing in grapevine. Phytopathology, 114
Reisch, B. I., Cadle-Davidson, L., Ikeogu, U., Sacks, G. L., Londo, J. P., & Martinson, T. E. (2023). Contributions of the VitisGen2 project to grapevine breeding and genetics. Vitis, 62.
Bierman, A., LaPlumm, T., Cadle-Davidson, L., Gadoury, D., Martinez, D., Sapkota, S., & Rea, M. (2019). A high-throughput phenotyping system using machine vision to quantify severity of grapevine powdery mildew. Plant Phenomics.