曹晶晶,王一鸣,毛文华,张小超.基于纹理和位置特征的麦田杂草识别方法[J].农业机械学报,2007,38(4):107-110. .[J].Transactions of the Chinese Society for Agricultural Machinery,2007,38(4):107-110.
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Abstract:7%。 Take the weeds in wheat fields as the research object, a method of weed detection by using the texture and position features was studied. According to the position feature of drilled crops that were regularly sown as a constant row space, the pixel-histogram method was used to determine the central line and the width of crop row. As a result, weeds between crop rows were detected. Moreover, the block of texture was selected on the basis of the central line of crop row. The co-occurrence matrixes of the H color space that was quantified 8 levels were computed. Based on that, five texture parameters were extracted. Then, the K means clustering method was used to recognize weeds within crop rows. The result of research showed that the correct classification of weeds was 93% and the mistake classification of crops was 7%. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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