TY - GEN
T1 - A non-linear model of nondestructive estimation of anthocyanin content in grapevine leaves with visible/red-infrared hyperspectral
AU - Qin, Jiang Lin
AU - Rundquist, Donald
AU - Gitelson, Anatoly
AU - Tan, Zongkun
AU - Steele, Mark
PY - 2011
Y1 - 2011
N2 - The anthocyanin(Anth) content in leaves provides valuable information about the physiologocal status of plant. Thus, there is a need for accurate, efficient, practical methodologies to estimate this biochemical parameter. Hyperspectral measurement is a means of quickly and nondestructively assessing leaf Anth in situ. Wet chemical methods has traditionally been used for this purpose. Recently, NIR(near-infrared)/green, red/green, anthocyanin reflectance index(ARI), and a modified anthocyanin refelctance index(MARI) was been used to estimate the anthocyanin content. In this paper, a an artificial-intelligence technique model was introduced to establish the relationship between the anthocyanin content and reflectance of 400-750nm spectum, variation of species and growth stages. The objective of this study was to test the overall performance and accuracy of this new nondestructive techniques for estimating Anth content in grapevine leaves. Although Anth in validation data set was widely variable, the new methods were capable of accurate predicting Anth content in grapevine leaves with a root mean square error below 1.65 mg/m 2, which is lower than that of MARI or ARI [20]. It documents the facts that such an approach is more suitable for developing simple hand-held field instrumentation for accurate nondestructive Anth estimation and for analyzing digital airborne or satellite imagery to assist in making informed decisions vineyard management.
AB - The anthocyanin(Anth) content in leaves provides valuable information about the physiologocal status of plant. Thus, there is a need for accurate, efficient, practical methodologies to estimate this biochemical parameter. Hyperspectral measurement is a means of quickly and nondestructively assessing leaf Anth in situ. Wet chemical methods has traditionally been used for this purpose. Recently, NIR(near-infrared)/green, red/green, anthocyanin reflectance index(ARI), and a modified anthocyanin refelctance index(MARI) was been used to estimate the anthocyanin content. In this paper, a an artificial-intelligence technique model was introduced to establish the relationship between the anthocyanin content and reflectance of 400-750nm spectum, variation of species and growth stages. The objective of this study was to test the overall performance and accuracy of this new nondestructive techniques for estimating Anth content in grapevine leaves. Although Anth in validation data set was widely variable, the new methods were capable of accurate predicting Anth content in grapevine leaves with a root mean square error below 1.65 mg/m 2, which is lower than that of MARI or ARI [20]. It documents the facts that such an approach is more suitable for developing simple hand-held field instrumentation for accurate nondestructive Anth estimation and for analyzing digital airborne or satellite imagery to assist in making informed decisions vineyard management.
KW - Anthocyanin
KW - Grapes
KW - Hyperspectral
KW - SVM (Support Vector Machine)
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U2 - 10.1007/978-3-642-18369-0_6
DO - 10.1007/978-3-642-18369-0_6
M3 - Conference contribution
AN - SCOPUS:79951623035
SN - 9783642183683
T3 - IFIP Advances in Information and Communication Technology
SP - 47
EP - 62
BT - Computer and Computing Technologies in Agriculture IV - 4th IFIP TC 12 Conference, CCTA 2010, Selected Papers
PB - Springer New York LLC
ER -