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Automatic Detection of the Main Vine and Branches of Tomato Plants Grown in Greenhouses

机译:在温室种植的番茄植物的主要藤蔓和分支的自动检测

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An algorithm for real-time detection of plants main vine and branches using the Distance Regularized Level Set Evolution (DRLSE) algorithm is presented in this paper. The algorithm tracks the target plant in the foreground from a set of seeding points automatically located on the main vine. The location of the seeding pints is determined in two different ways using the Hough transform and template searching. Test images are captured inside a greenhouse and include a huge amount of clutter from adjacent plants. The clutter creates a complex environment for traditional segmentation techniques to detect branches of the plant in the foreground. Images are normalized to speed the execution time of the DRLSE and protect the algorithm from local minima traps. Results demonstrate the capability of the algorithm in detecting the main vine and major branches. This algorithm is good for real-time maintenance (i.e., deleafing) using robots inside greenhouses.
机译:本文介绍了一种使用距离正则级集合(DRLSE)算法的植物主藤和分支的实时检测算法。该算法从一组自动位于主藤上的播种点跟踪目标植物。使用Hough变换和模板搜索以两种不同的方式确定播种品脱的位置。测试图像在温室内捕获,并包括相邻植物的大量杂乱。杂乱为传统的分割技术创造了一个复杂的环境,以检测前景中植物的分支。图像被归一化以加速DRLSE的执行时间并保护算法免受局部最小陷阱。结果展示了算法在检测主藤和主要分支方面的能力。这种算法适用于使用温室内的机器人的实时维护(即,百曲)。

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