From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
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.
Tiebiao Zhao, Zhongdao Wang, Qi Yang, and YangQuan Chen, "Melon yield prediction using small unmanned aerial vehicles," Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 1021808 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 10, 2017; Published: 16 May 2017); https://doi.org/10.1117/12.2262412.
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