Share Email Print
cover

Proceedings Paper

Melon yield prediction using small unmanned aerial vehicles
Author(s): Tiebiao Zhao; Zhongdao Wang; Qi Yang; YangQuan Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.

Paper Details

Date Published: 16 May 2017
PDF: 6 pages
Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 1021808 (16 May 2017); doi: 10.1117/12.2262412
Show Author Affiliations
Tiebiao Zhao, Univ. of California, Merced (United States)
Zhongdao Wang, Tsinghua Univ. (China)
Qi Yang, Shenyang Ligong Univ. (China)
YangQuan Chen, Univ. of California, Merced (United States)


Published in SPIE Proceedings Vol. 10218:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

© SPIE. Terms of Use
Back to Top