Robust crop and weed segmentation under uncontrolled outdoor illumination.
Robust crop and weed segmentation under uncontrolled outdoor illumination.
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DOI：
10.3390/s110606270
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Zhu H
中科院分区：
文献类型：
--
作者：
Jeon HY;Tian LF;Zhu H
提出了一种用于杂草检测的图像处理算法，并对其进行了评价。杂草检测过程包括归一化过绿转换、统计阈值估计、自适应图像分割、中值滤波、形态特征计算和人工神经网络(ANN)。所开发的算法被验证其识别和检测杂草和农作物在不受控制的室外光照下的能力。利用机器视觉实现野外机器人在室外光照下采集田间图像，图像处理算法无需人工调整即可自动处理。当处理666幅现场图像时，该算法的误差在2.1%到2.9%之间。人工神经网络从识别出的植物中正确检测出72.6%的作物，其余的被认为是杂草。然而，通过解决算法中的误差源，使作物的ANN识别率提高到95.1%。所开发的杂草检测和图像处理算法为在不受控制的室外光照下根据土壤背景识别植物以及区分杂草和农作物提供了一种新的方法。因此，提出的新的机器视觉和处理算法可能对包括植物特定直接应用(PSDA)在内的户外应用有用。
An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).
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DOI：
10.1006/jaer.1996.0114
发表时间：
1997-01-01
期刊：
JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH
影响因子：
--
作者：
Ramon, H;DeBaerdemaeker, J
通讯作者：
DeBaerdemaeker, J
影响因子：
5.1
作者：
Cho, SI;Lee, DS;Jeong, JY
通讯作者：
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DOI：
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发表时间：
2002-09-01
期刊：
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影响因子：
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作者：
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通讯作者：
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影响因子：
6.2
作者：
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通讯作者：
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影响因子：
8.3
作者：
Tian, LF;Slaughter, DC
通讯作者：
Slaughter, DC