Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, JAPAN.
I am currently involved in the development of methods that leverage artificial intelligence to tackle agricultural issues. One example is the identification of significant agricultural organisms through image analysis and the detection of diseases and pests on crop leaves. Furthermore, I am working on techniques to predict the severity of future disease and pest outbreaks by utilizing open data from past outbreaks.
- Akiyama R, Goto T, Tameshige T, Sugisaka J, Kuroki K, Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation. Nat. Commun., 2023, 14:5792. doi: 10.1038/s41467-023-41260-3 , Akita J, Hatakeyama M, Kudoh H, Kenta T, Tonouchi A, Shimahara Y, Sese J, Kutsuna N, Shimizu-Inatsugi R*, Shimizu KK*.
- Kishi S*, Characteristic features of statistical models and machine learning methods derived from pest and disease monitoring datasets. Royal Soc. Open Sci., 2023, doi: 10.1098/rsos.230079 , Kawaguchi A, Ochi S, Yoshida M, Yamanaka T,
- Few-shot learning for plant disease recognition: A review. Agron. J., 2023, doi: 10.1002/agj2.21285 10.1002/csan.21101 *, Cao W, Fu X, Ochi S, 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 *, Cao W, 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 *, Futahashi R, Yamanaka T.
Plant Science & Bioinformatics
I am actively involved in the analysis of plant RNA-seq data to gain a deeper understanding of gene function, and I am also dedicated to developing bioinformatics methods and tools specifically designed for this purpose. One of my primary research focuses lies in the realm of allopolyploid plants, which are formed through the hybridization of closely related species, such as wheat and bittercresses. Additionally, I am highly interested in analyzing RNA-seq data derived from viroids, which are the smallest known pathogens that infect plants.
- A low-coverage 3′ RNA-seq to detect homeolog expression in polyploid wheat. NAR Genomics and Bioinformatics., 2023, 5(3):1. doi: 10.1093/nargab/lqad067 , Okada M, Tameshige T, Shimizu-Inatsugi, Akiyama, Nagano AJ, Sese J, Shimizu KK*.
- Akiyama 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 , Hatakeyama M, Lischer HEL, Briskine RV, Hay A, Gan X, Tsiantis M, Kudoh H, Kanaoka M M, Sese J, Shimizu KK, Shimizu-Inatsugi R*.
- 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 , Shimizu-Inatsugi R, Hofhuis H, Shimizu K, Hay A, Shimizu KK, Sese J*.
- TCC: an R package for comparing tag count data with robust normalization strategies. BMC Bioinform., 2013, 14:219. doi: 10.1186/1471-2105-14-219 , Nishiyama T, Shimizu K, Kadota K*.