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IN-FIELD RECOGNITION AND NAVIGATION PATH EXTRACTION FOR PINEAPPLE HARVESTING ROBOTS

机译:菠萝捕捞机器人的现场识别和导航路径提取

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Fruit recognition and navigation path extraction are important issues for developing fruit harvesting robots. This manuscript presents a recent study on developing an algorithm for recognizing "on-the-go" pineapple fruits and the cultivation rows for a harvesting robotic system. In-field pineapple recognition can be difficult due to many overlapping leaves from neighbouring plants. As pineapple fruits (Ananas comosus) are normally located at top of the plant with a crowned by a compact tuft of young leaves, image processing algorithms were developed to recognize the crown to locate the corresponding pineapple fruit in this study. RGB (Red, Green, and Blue) images were firstly collected from top-view of pineapple trees in the field and transformed into HSI (Hue, Saturation and Intensity) colour model. Then, Features of pineapple crowns were extracted and used for developing a classification algorithm. After the pineapple crowns were recognized, locations of the crowns grown in one row were determined and linearly fitted into a line, which could be used for navigating the harvesting robots to conduct the harvest. To validate the above algorithms, 100 images were taken in a pineapple field under different environments in Guangdong province as a validation set. The results showed that pineapple recognition rate can reach 94% on clear sky day, which was much better than that on overcast sky day and the navigation path was well fitted.
机译:水果识别和导航路径提取是开发水果收获机器人的重要问题。该手稿提出了一项最新研究,该研究开发了一种用于识别“移动中”菠萝果实和收获机器人系统的栽培行的算法。由于相邻植物的叶片重叠,田间菠萝识别可能很困难。由于菠萝果实(Ananas comosus)通常位于植物的顶部,树冠冠上有紧密的嫩叶簇,因此在本研究中开发了图像处理算法以识别树冠以定位相应的菠萝果实。首先从野外菠萝树的顶视图收集RGB(红色,绿色和蓝色)图像,并将其转换为HSI(色相,饱和度和强度)颜色模型。然后,提取菠萝冠的特征并将其用于开发分类算法。识别出菠萝冠之后,确定在一行中生长的冠的位置,并将其线性地装配成一条线,该线可用于导航收割机器人以进行收割。为了验证上述算法,在广东省不同环境的菠萝田中拍摄了100张图像作为验证集。结果表明,晴天时菠萝的识别率可以达到94%,比阴天时的菠萝识别率要好得多,导航路径也很合适。

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