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

Jun Sun, Xiaofei He, Xiao Ge, Xiaohong Wu, Jifeng Shen and Yingying Song
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Jun Sun: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
Xiaofei He: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
Xiao Ge: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
Xiaohong Wu: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
Jifeng Shen: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
Yingying Song: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China

Agriculture, 2018, vol. 8, issue 12, 1-15

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.

Keywords: object detection; tomato organ; K-means clustering; Soft-NMS; migration learning; convolutional neural network; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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