Next Article in Journal
Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea
Previous Article in Journal
Methods for the Diagnosis of Grapevine Viral Infections: A Review
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle

Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background

School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Agriculture 2018, 8(12), 196; https://doi.org/10.3390/agriculture8120196
Received: 21 October 2018 / Revised: 5 December 2018 / Accepted: 7 December 2018 / Published: 11 December 2018
  |  
PDF [7318 KB, uploaded 11 December 2018]
  |     |  

Abstract

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomatoes and the plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rates and poor generalizations of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network (CNN) has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and a K-means clustering method was used to adjust more appropriate anchor sizes than manual setting, to improve detection accuracy. The test results showed that the mean average precision (mAP) was significantly improved compared with the traditional Faster R-CNN model. The training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of a precise targeting pesticide application system and an automatic picking device. View Full-Text
Keywords: object detection; tomato organ; K-means clustering; Soft-NMS; migration learning; convolutional neural network; deep learning object detection; tomato organ; K-means clustering; Soft-NMS; migration learning; convolutional neural network; deep learning
Figures

Figure 1

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 (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Sun, J.; He, X.; Ge, X.; Wu, X.; Shen, J.; Song, Y. Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background. Agriculture 2018, 8, 196.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Agriculture EISSN 2077-0472 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top