Shape Features for Identifying Young Weeds Using Image Analysis
计算机科学
模式识别(心理学)
计算机视觉
杂草
图像(数学)
作者
D. M. Woebbecke,George E. Meyer,K. Von Bargen,D. A. Mortensen
出处
期刊:Transactions of the ASAE [American Society of Agricultural and Biological Engineers] 日期:1995-01-01卷期号:38 (1): 271-281被引量:189
标识
DOI:10.13031/2013.27839
摘要
Shape feature analyses were performed on binary images originally obtained from color images of 10 common weeds, along with corn and soybeans, found in the Midwest. Features studied were roundness, aspect, perimeter/thickness, elongatedness, and seven invariant central moments (ICM), for each plant type and age up to 45 days after emergence. Shape features were generally independent of plant size, image rotation, and plant location within most images. The ability to discriminate between monocots and dicots was most evident between 14 and 23 days using these features. Shape features that best distinguished these plants were aspect and first invariant central moment (ICM1), which classified 60 to 90% of the dicots from the monocots. Using Analysis of Variance and Tukeys multiple comparison tests, shape features did not change significantly for most species over the study period. This information could be very useful in the future design of advanced spot spraying applications.