Autosoft Journal

Online Manuscript Access

In-Field Recognition and Navigation Path Extraction For Pineapple Harvesting Robots



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.



Total Pages: 9
Pages: 99-107


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 19
Issue: 1
Year: 2013

Cite this document


Zhao Y. Transactions of the Chinese Society of Agricultural Engineering

Zhao Q. B. Journal of Agricultural Mechanization Research

Liu C. L. Anhui agricultural science

Jiang G. Q. Journal of Agricultural Mechanization Research

Hayashi S. Biosystems Engineering

Zhang F. M. Study on algorithms of field road detection and stereovision-based guidance algorithm for a field vehicle

Van Henten E. J. Biosystems Engineering

Xiang R. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering

Zhang Y. J. Acta Optica Sinica

Jiang H. Y. Journal of Jiangsu University (Natural Science Edition)

Sonka M. Image Processing, Analysis, and Machine Vision (Second Edition)

Li G. Transactions of the Chinese Society for Agricultural Machinery

Liu C. L. Transactions of the Chinese Society for Agricultural Machinery

He, X. L., Du, S. F. & Jiang, G. Q. (2007). Two navigation extraction methods based on machine vision. The 5th national information acquisition and processing conference

Wang X. J. Science & Technology Information

Kise M. Biosystems Engineering

Van, K., Crisan, L. M., Bontsema, J. & Wennekes, P. (1998). Mechatronic system for automatic harvesting of cucumbers. IFAC Control Applications and Ergonomics in Agriculture (pp. 289–293). Athens, Greece

Li Z. Journal of Food Engineering

Bulanon D. M. Biosystems Engineering


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048