Agriculture × Artificial Intelligence
I am working on developing methods that utilise artificial intelligence to address agricultural problems. For instance, I am working on identifying important agricultural organisms from images and detecting crop diseases and pests on leaves, flowers, and fruits. Additionally, I am developing techniques to forecast the magnitude of future disease and pest outbreaks by utilising open data on past outbreaks.
- Sun J*, Cao W, Fu X, Ochi S, Yamanaka T. Few-shot learning for plant disease recognition: A review. Agron. J., 2023, doi: 10.1002/agj2.21285
- Sun J*, Cao W, Yamanaka T. JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis. Front. Plant Sci., 2022, 13:964058. doi: 10.3389/fpls.2022.964058
- Sun J*, Futahashi R, Yamanaka T. Improving the accuracy of species identification by combining deep learning with field occurrence records. Front. Ecol. Evol., 2021, 9:762173. doi: 10.3389/fevo.2021.762173
Plant Science × Bioinformatics
In addition to my work in agriculture, I am also analysing plant RNA-seq data to better understand gene function and developing bioinformatics methods as well as tools for this purpose. My focus is on allopolyploid plants, which result from the hybridization of closely related species, such as wheat and bittercresses. I'm also interested in analysing RNA-seq data from viroids, the smallest known pathogen infecting plants.
- Akiyama R, Sun J, Hatakeyama M, Lischer HEL, Briskine RV, Hay A, Gan X, Tsiantis M, Kudoh H, Kanaoka M M, Sese J, Shimizu KK, Shimizu-Inatsugi R*. Fine-scale empirical data on niche divergence and homeolog expression patterns in an allopolyploid and its diploid progenitor species. New Phytol., 2021, 229(6):3587-3601. doi: 10.1111/nph.17101
- Sun J, Shimizu-Inatsugi R, Hofhuis H, Shimizu K, Hay A, Shimizu KK, Sese J*. A recently formed triploid Cardamine insueta inherits leaf vivipary and submergence tolerance traits of parents. Front. Genet., 2020, 11:567262. doi: 10.3389/fgene.2020.567262
- Su W, Sun J, Shimizu K, Kadota K*. TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data. BMC Res. Notes, 2019, 12:133. doi: 10.1186/s13104-019-4179-2