郭寶錚教授

學歷

美國Virginia Commonwealth Univ. 生物統計系博士

美國Iowa State Univ. 統計系碩士

國立中興大學糧食作物研究所碩士

國立中興大學農藝系學士

經歷

國立中興大學農藝系 教授 (2002年08月迄今)

國立中興大學農藝系 系主任 (2004年11月至2007年07月；2016年10月至2020年01月)

國立中興大學植物醫學暨安全農業碩士學位學程 主任 (2017年08月至2018年07月)

國立中興大學農業試驗場 場長 (2002年08月至2005年07月)

研究領域

生物統計、試驗設計、NIR光譜資料處理、淨最小平方法之研究、作物生長模式、模擬基改作物花粉飄散、量測不確定度的評估、智慧科技在農業生產上之應用

近五年期刊論文

• 杜元凱、陳涵葳、紀銘坤、方士倫、姚銘輝、郭寶錚。2022。利用高光譜資料建立小果番茄缺水逆境早期預測模型。台灣農業研究，71(2): 87-99。

• 林詠淳、康樂、郭寶錚。2020。隨機森林的理論及應用範例說明。作物、環境與生物資訊，17: 1-12。

• 杜元凱、陳涵葳、洪逸筑、林彥君、郭寶錚。2019。準確偵測冬季裡作偶然存在大、小油菜雜交後代之方法。臺灣農業研究，68(1): 69-77

• 林煒倫、曾偉誠、許奕婷、蔡慧萍、許鈺群、郭寶錚、楊靜瑩、楊明德。2019。無人飛行載具應用於水稻秧苗缺株率之影像判釋。作物、環境與生物資訊，16(2): 63-71

• 吳郁嫻、許奕婷、陳忠澤、吳佩真、吳東鴻、賴明信、郭寶錚、陳宗禮、楊靜瑩。2019。透過田間智能水位感測管理水稻灌溉用水量及稻株生理性狀與產量變化探討。作物、環境與生物資訊，16(2): 87-97

• Su, Y. C., Wu, C. Y., & Kuo, B. J. (2024). Characterizing spatiotemporal patterns of disasters and climates to evaluate hazards to crop production in Taiwan. Agriculture, 14(8), 1384. https://doi.org/10.3390/agriculture14081384

• Fang, S. L., Lin, Y. S., Chang, S. C., Chang, Y. L., Tsai, B. Y., & Kuo, B. J. (2024). Using artificial intelligence algorithms to estimate and short-term forecast the daily reference evapotranspiration with limited meteorological variables. Agriculture, 14(4), 510. https://doi.org/10.3390/agriculture14040510

• Lin, Y. S., Fang, S. L., Kang, L., Chen, C. C., Yao, M. H., & Kuo, B. J. (2024). Combining recurrent neural network and sigmoid growth models for short-term temperature forecasting and tomato growth prediction in a plastic greenhouse. Horticulturae, 10(3), 230. https://doi.org/10.3390/horticulturae10030230

• Su, Y. C., Shen, Y., Wu, C. Y., & Kuo, B. J. (2024). County-scale dataset indicating the effects of disasters on crops in Taiwan from 2003 to 2022. Scientific Data, 11, 205. https://doi.org/10.1038/s41597-024-03053-1

• Fang, S. L., Cheng, Y. J., Tu, Y. K., Yao, M. H., & Kuo, B. J. (2023). Exploring efficient methods for using multiple spectral reflectance indices to establish a prediction model for early drought stress detection in greenhouse tomato. Horticulturae, 9(12), 1317.

• Kuo, C. E., Tu, Y. K., Fang, S. L., Huang, Y. R., Chen, H. W., Yao, M. H., & Kuo, B. J. (2023). Early detection of drought stress in tomato from spectroscopic data: A novel convolutional neural network with feature selection. Chemometrics and Intelligent Laboratory Systems, 104869.

• Su, Y. C. & Kuo, B. J. (2023). Risk assessment of rice damage due to heavy rain in Taiwan. Agriculture, 13(3), 630.

• Fang, S. L., Tu, Y. K., Kang, L., Chen, H. W., Chang, T. J., Yao, M. H. & Kuo, B. J. (2023). CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf–air temperature difference. Sci Rep, 13, 602. https://doi.org/10.1038/s41598-023-27798-8

• Fang, S. L., Kuo, Y. H., Kang, L., Chen, C. C., Hsieh, C. Y., Yao, M. H., & Kuo, B. J. (2022). Using sigmoid growth models to simulate greenhouse tomato growth and development. Horticulturae, 8(11), 1021.

• Tu, Y. K., Kuo, C. E., Fang, S. L., Chen, H. W., Chi, M. K., Yao, M. H., & Kuo, B. J. (2022). A 1D-SP-Net to determine early drought stress status of tomato (Solanum lycopersicum) with imbalanced Vis/NIR spectroscopy data. Agriculture, 12(2), 259. https://doi.org/10.3390/agriculture12020259

• Fang, S. L., Chang, T. J., Tu, Y. K., Chen, H. W., Yao, M. H., & Kuo, B. J. (2022). Plant-response-based control strategy for irrigation and environmental controls for greenhouse tomato seedling cultivation. Agriculture, 12(5), 633.

• Hsieh, C. Y., Fang, S. L., Wu, Y. F., Chu, Y. C., & Kuo, B. J. (2021). Using sigmoid growth curves to establish growth models of tomato and eggplant stems suitable for grafting in subtropical countries. Horticulturae, 7(12), 537.

• Jhong, Y. S., Lin, W. S., Yiu, T. J., Su, Y. C., & Kuo, B. J. (2021). Effectiveness of different sampling schemes in predicting adventitious genetically modified maize content in a smallholder farming system. GM Crops & Food, 12(1), 212-223.

• Kuo, B. J., Jhong, Y. S., Yiu, T. J., Su, Y. C., & Lin, W. S. (2021). Bootstrap simulations for evaluating the model estimation of the extent of cross-pollination in maize at the field-scale level. PloS one, 16(5), e0249700.

• Li, G. S., Wu, D. H., Su, Y. C., Kuo, B. J., Yang, M. D., Lai, M. H., Lu, H. Y., & Yang, C. Y. (2021). Prediction of plant nutrition state of rice under water-saving cultivation and panicle fertilization application decision making. Agronomy, 11(8), 1626.

• Su, Y. C., Lee, C. B., Yiu, T. J., & Kuo, B. J. (2021). Incorporating the field border effect to reduce the predicted uncertainty of pollen dispersal model in Asia. Scientific Reports, 11, 22187.

• Tu, Y. K., Chen, H. W., Fang, S. L., Yao, M. H., Tseng, Y. Y., & Kuo, B. J. (2021). Establishing of early discrimination methods for drought stress of tomato by using environmental parameters and NIR spectroscopy in greenhouse. Acta Hortic. 1311, 501-512

• Tu, Y. K., Chen, H. W., Tseng, K. Y., Lin, Y. C., Kuo, B. J. (2020). Morphological and genetic characteristics of F1 hybrids introgressed from Brassica napus to B. rapa in Taiwan. Botanical Studies, 61:1. https://doi.org/10.1186/s40529-019-0279-5

• Su, Y. C., Wang, P. S., Yang, J. L., Hong, H., Lin, T. K., Tu, Y. K., Kuo, B. J. (2020). Using a zero-inflated model to assess gene flow risk and coexistence of Brassica napus L. and Brassica rapa L. on a field scale in Taiwan. Botanical Studies, 61:17. https://doi.org/10.1186/s40529-020-00294-2

• Laffont, J. L., Hong, B., Kuo, B. J., & Remund, K. M. (2019). Exact theoretical distributions around the replicate results of a germination test. Seed Science Research, 29: 64-72