- freely available
On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods
AbstractFully 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.
Share & Cite This Article
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.View more citation formats
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.
Notes: Multiple requests from the same IP address are counted as one view.