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Growth Behavior of Ragweed (Ambrosia Artemisiifolia L.) on Agricultural Land in Brandenburg (Germany)-Conclusions for Image Analysis in Camera Based Monitoring Strategies

机译:基于相机监测策略的勃兰登堡(德国)农业土地农业用地农业用地的生长行为 - 基于相机的图像分析

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摘要

A requirement to prevent a further spreading of ragweed (Ambrosia artemisiifolia L.), a neophyte to Europe, is an effective monitoring to find single or clustered plants within the natural environment. Noncontact camera sensors attached to agricultural, municipal or aircraft vehicles are an adequate tool to survey large areas of landscape. In result a massive number of images has to be analyzed (if possible in real time) whether ambrosia tissue is present or absent in the respective image. In agricultural fields it would be utile if the imaging is connected with field operations, which had to be done anyway. Subsequently, changing imaging conditions occur. The appearance of the ambrosia and the co-socialized plant species show specific characteristics which can be typically described by metadata. Regarding image analysis various methods exist for object classification which has situation dependent advantages and disadvantages. At the present state of knowledge in the near future an all-purpose classification method under constantly variable environment conditions will not exist. In the paper examples for the appearance of the ambrosia plant in different agricultural fields at different times in the year are presented. According to the situation it follows up a suitable image analysis which classify tissue of the ambrosia. Advantages and disadvantages are discussed. A perspective for a metadata adapted image analysis is shown.
机译:要求防止豚草(Ambrosia Artemisiifolia L.)的进一步扩散到欧洲,是一种有效的监测,在自然环境中找到单一或聚类植物。附加到农业,市政或航空器车辆的非接触式摄像机传感器是一个足够的工具来调查大面积的景观。结果,必须分析大量的图像(如果可以实时地)在各个图像中是否存在或不存在氨胃组织。在农业领域,如果成像与现场操作有关,这将是简洁的,这必须是无论如何都要完成的。随后,发生变化的成像条件。 ambrosia和共同社交植物物种的外观表明了可以通过元数据描述的具体特征。关于图像分析,对象分类存在各种方法,其具有依赖性优缺点和缺点。在目前的知识状态下,在不久的将来的近期可变环境条件下的通用分类方法将不存在。在本年度不同时期不同农业领域的ambrosia植物的纸张实例。根据情况,它跟踪适当的图像分析,该图像分析分类了ambrosia的组织。讨论了优点和缺点。显示了元数据适应图像分析的透视图。

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