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

文献类型： 外文期刊

作者： Li, Bin 1 ; Wang, Maohua 2 ;

作者机构： 1.China Natl Engn Res Ctr Informat Technol Agr, Intelligent Detect Dept, Beijing, Peoples R China

2.China Agr Univ, Coll Informat & Elect Engn, Beijing 100094, Peoples R China

关键词： Computer vision; pineapple harvesting robot; color model; navigation path

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：1.647； 五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2013 年 19 卷 1 期

页码：

收录情况： SCI

摘要： 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. RUB (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.

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