Machine-vision weed density estimation for real-time, outdoor lighting conditions
Machine-vision weed density estimation for real-time, outdoor lighting conditions
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实时室外照明条件下的机器视觉杂草密度估计
DOI：
10.13031/2013.13355
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发表时间：
1999
影响因子：
1.5
通讯作者：
L. Tian
中科院分区：
文献类型：
--
作者：
B. Steward;L. Tian
开发了一个系统来估计两行大豆之间的杂草密度。环境的
采用自适应分割算法（EASA）将植物从图像背景中分割出来。的影响
研究了两种图像数据变换对EASA分割性能的影响，并对RGB-IV 1V 2
变换导致基于形态学开闭的分割结果的质量显著提高
RGB-RGB变换的像素损失。开发了一种自适应扫描算法（阿萨），
自动检测作物行间边缘并估计行间区域的杂草数量。两组图像
在晴天和阴天条件下采集。ASA检测的作物行边缘位置显著高于对照，
与手动检测到的作物行位置相关，分布偏向于作物行内部的位置。
排阿萨杂草密度估计与手动杂草计数两种照明条件下高度相关。然而，在这方面，
当考虑有限范围的数据时，相关性要低得多，揭示了空间色彩的损失
分辨率由于视频信号的传输。阿萨的平均执行时间为0.038秒，为0.91米（3英尺）
长的行间区域，表明该算法满足了用作传感系统所需的实时约束
一种可变剂量除草剂施用器
A system to estimate the weed density between two rows of soybeans was developed. An environmentally
adaptive segmentation algorithm (EASA) was used to segment the plants from the background of the image. The effect of
two image data transformations on the segmentation performance of the EASA was investigated, and the RGB-IV1V2
transformation resulted in significantly higher quality segmentation results based on morphological opening and closing
pixel loss over the RGB-rgb transformation. An adaptive scanning algorithm (ASA) was developed and used to
automatically detect crop inter-row edges and to estimate the number of weeds in the inter-row area. Two sets of images
were acquired under sunny and overcast sky conditions. The ASA-detected crop row edge positions were significantly
correlated with the manually detected crop row positions, with the distribution skewed towards positions internal to the
row. ASA weed density estimates were highly correlated with manual weed counts for both lighting conditions. However,
when a limited range of the data was considered, much lower correlations resulted, revealing a loss of spatial color
resolution due to the transmission of the video signal. The mean execution time of the ASA was 0.038 s for 0.91 m (3 ft)
long inter-row regions showing that the algorithm met the real-time constraints necessary to be used as a sensing system
for a variable-rate herbicide applicator.