Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting
Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting
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DOI：
10.3390/app10175887
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发表时间：
2020-09-01
影响因子：
2.7
通讯作者：
Rodriguez, F.
中科院分区：
文献类型：
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作者：
Benavides, M.;Canton-Garbin, M.;Rodriguez, F.
保护地农业是一个自动化系统的使用是一个关键因素的领域。事实上，精致水果的自动收获尚未完善。在过去的四十年里，这个问题受到了极大的关注，尽管目前还没有商业化的收割机器人，主要是由于工作环境的复杂性和多变性。在这项工作中，我们开发了一个计算机视觉系统（CVS），在一个典型的地中海温室番茄作物的水果自动检测和定位。系统要执行的任务是：（1）检测成熟的番茄，（2）在图像的XY坐标中定位成熟的番茄，以及（3）在图像的XY坐标中定位成熟的番茄的梗。任务1和任务2使用大量的数字图像处理工具（增强、边缘检测、分割和番茄的特征描述）来执行。任务3使用基本三角学以及数字和几何描述符进行。结果是非常有希望的牛肉和西红柿簇，该系统能够分类80.8%和87.5%，分别为“可收集”的水果与可见的花梗。对于可见的成熟和收获的番茄，每个图像的平均处理时间小于30 ms。
Protected agriculture is a field in which the use of automatic systems is a key factor. In fact, the automatic harvesting of delicate fruit has not yet been perfected. This issue has received a great deal of attention over the last forty years, although no commercial harvesting robots are available at present, mainly due to the complexity and variability of the working environments. In this work we developed a computer vision system (CVS) to automate the detection and localization of fruit in a tomato crop in a typical Mediterranean greenhouse. The tasks to be performed by the system are: (1) the detection of the ripe tomatoes, (2) the location of the ripe tomatoes in the XY coordinates of the image, and (3) the location of the ripe tomatoes' peduncles in the XY coordinates of the image. Tasks 1 and 2 were performed using a large set of digital image processing tools (enhancement, edge detection, segmentation, and the feature's description of the tomatoes). Task 3 was carried out using basic trigonometry and numerical and geometrical descriptors. The results are very promising for beef and cluster tomatoes, with the system being able to classify 80.8% and 87.5%, respectively, of fruit with visible peduncles as "collectible". The average processing time per image for visible ripe and harvested tomatoes was less than 30 ms.