Jianqiang Sun
Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, JAPAN.
Researches
I work on AI applications in agriculture, including plant disease detection and predictive modeling of pest and disease outbreaks. Though, to be honest, these projects are more of an institutional mission than a personal passion. My real interest lies in gene expression analysis of allopolyploid plants, like canola, coffee, and wheat, and in developing bioinformatics tools to study such complex biological data. Officially, I earn my salary from institutional projects; unofficially, I spend most of my time chasing genes. Hopefully my facility directors don’t see this CV.
- , Sese J, Shimizu KK. A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids. New Phytol., 2025, doi: 10.1111/nph.70648 bioinformatics statistics
- , Ochi S, Yamanaka T. Analysis of crop disease and pest occurrences: Insights from Japan’s national surveys. PLoS ONE., 2025, 20(4):e0322579. doi: 10.1371/journal.pone.0322579 statistics machine learning
- . Addressing domain shift in deep learning: Challenges and insights from plant disease diagnosis and flower recognition. bioRixv [PREPRINT], 2024. doi: 10.1101/2024.10.07.617111 machine learning
- , Matsushita Y. Predicting symptom severity in PSTVd‐infected tomato plants using the PSTVd genome sequence. Mol. Plant Pathol., 2024, 25(7):e13469. doi: 10.1111/mpp.13469 bioinformatics machine learning
- , Okada M, Tameshige T, Shimizu-Inatsugi, Akiyama, Nagano AJ, Sese J, Shimizu KK. A low-coverage 3′ RNA-seq to detect homeolog expression in polyploid wheat. NAR Genom Bioinform., 2023, 5(3):1. doi: 10.1093/nargab/lqad067 bioinformatics
- , Cao W, Fu X, Ochi S, Yamanaka T. Few-shot learning for plant disease recognition: A review. Agron. J., 2023, 116(3):1204-1216. doi: 10.1002/agj2.21285 machine learning
- , 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 machine learning
- Akiyama R, , 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 bioinformatics
- , 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 bioinformatics