Sensors 2014, 14(7), 12191-12206; doi:10.3390/s140712191
Article

On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods

1email, 1email, 2email and 1,* email
Received: 4 May 2014; in revised form: 16 June 2014 / Accepted: 24 June 2014 / Published: 9 July 2014
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.
Keywords: image analysis; fruit detection; machine learning; young fruit; tomato
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MDPI and ACS Style

Yamamoto, K.; Guo, W.; Yoshioka, Y.; Ninomiya, S. On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods. Sensors 2014, 14, 12191-12206.

AMA Style

Yamamoto K, Guo W, Yoshioka Y, Ninomiya S. On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods. Sensors. 2014; 14(7):12191-12206.

Chicago/Turabian Style

Yamamoto, Kyosuke; Guo, Wei; Yoshioka, Yosuke; Ninomiya, Seishi. 2014. "On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods." Sensors 14, no. 7: 12191-12206.

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