Abstract : In order to overcome the low diagnostic accuracy of traditional blast visual defects, set cold rice blast for example, four data groups has been acquainted through near infrared spectroscopy, the first group is healthy and diseased planting stock; the second group is three diseased levels of leaf blast; the third group is five diseased levels of grain blast; the forth group is four diseased levels of panicle blast, according to the analysis, we can know that different circumstances of plants had their own near-infrared spectral bands which made the preliminary basis for real-time detection of cold Rice blast.
Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-369, pp.64-71, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27278-3_8〉
https://hal.inria.fr/hal-01360965
Contributeur : Hal Ifip
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Soumis le : mardi 6 septembre 2016 - 15:03:21
Dernière modification le : mardi 6 septembre 2016 - 16:07:32
Document(s) archivé(s) le : mercredi 7 décembre 2016 - 13:13:32
Feng Tan, Xiaodan Ma, Chun Wang, Tingyi Shang. Data Analysis of Cold Rice Blast Based on Near Infrared Spectroscopy. Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-369, pp.64-71, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27278-3_8〉. 〈hal-01360965〉
