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Open AccessArticle

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

Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midori-cho, Nishitokyo, Tokyo 188-0002, Japan
Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Author to whom correspondence should be addressed.
Sensors 2014, 14(7), 12191-12206;
Received: 4 May 2014 / Revised: 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)
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. View Full-Text
Keywords: image analysis; fruit detection; machine learning; young fruit; tomato image analysis; fruit detection; machine learning; young fruit; tomato
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.

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