@article{oai:repository.naro.go.jp:00003747,
 author = {佐野, 智人 and SANO, Tomohito and 堀江, 秀樹 and HORIE, Hideki},
 issue = {2},
 journal = {Canadian journal of remote sensing, Canadian journal of remote sensing},
 month = {Jun},
 note = {The highest quality green tea is cultivated using shading treatments in Japan; however, shading can lead to early mortalities of tea due to excessive environmental stress. The allocation of photosynthetic pigments, chlorophyll a, b and carotenoids, could be a good indicator for evaluating production or environmental stress in plants; thus, developing an in-situ method to monitor photosynthetic pigments is useful for agricultural management. To assess the accuracy of the estimation of photosynthetic pigment contents with existing supervised learning models, four different approaches were compared including random forests, kernel-based extreme learning machine (KELM), deep belief nets and support vector machine. Overall, KELM had the highest performance with a root mean square error of 1.95 ± 0.36 μg cm-2, 1.08 ± 0.11 μg cm-2 and 0.68 ± 0.10 μg cm-2 for estimating chlorophyll a, b and carotenoid contents, respectively.},
 pages = {104--112},
 title = {Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance},
 volume = {44},
 year = {2018},
 yomi = {サノ, トモヒト and ホリエ, ヒデキ}
}