期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 50, 期 2, 页码 121-134出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2005.09.004
关键词
Elliptic Fourier; discriminant analysis; leaves; machine vision; shape features
Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine incur (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pi.-weed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pi-weed, 93.6% of sunflower. 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping, (c) 2005 Elsevier B.V. All rights reserved.
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