Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 21 October 2018 / Approved: 23 October 2018 / Online: 23 October 2018 (07:57:44 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Agriculture 2018, 8, 196
DOI: 10.3390/agriculture8120196

Abstract

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomato and plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network 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 K-means clustering method was used to adjust more appropriate anchor size than manual setting to improve detection accuracy. A variety of data augmentation techniques were used to train the network. The test results showed that compared with the traditional Faster R-CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of precise targeting pesticide application system and automatic picking device.

Subject Areas

object detection; tomato organ; K-means clustering; Soft-NMS; migration learning; convolutional neural network; deep learning

Readers' Comments and Ratings (0)

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.