Abstract
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 g cm -2 , 1.08 0.11 g cm -2 and 0.68 0.10 g cm -2 for estimating chlorophyll a, b and carotenoid contents, respectively.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc-nd. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 3
Claim · model-written summary
KELM had the highest performance estimating chlorophyll a content, with a root mean square error of 1.95 0.36 g cm -2.
Verbatim
e0“KELM had the highest performance with a root mean square error of 1.95 0.36 g cm -2 , 1.08 0.11 g cm -2 and 0.68 0.10 g cm -2 for estimating chlorophyll a, b and carotenoid contents, respectively”
t0“Tea”
Claim · model-written summary
KELM estimated chlorophyll b with a root mean square error of 1.08 0.11 g cm -2.
Verbatim
e0“1.08 0.11 g cm -2 and 0.68 0.10 g cm -2 for estimating chlorophyll a, b and carotenoid contents, respectively”
t0“Tea”
Claim · model-written summary
KELM estimated carotenoids with a root mean square error of 0.68 0.10 g cm -2.
Verbatim
e0“0.68 0.10 g cm -2 for estimating chlorophyll a, b and carotenoid contents, respectively”
t0“Tea”