Proc. SPIE. 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
KEYWORDS: Unmanned aerial vehicles, Agriculture, Visual process modeling, Data modeling, Image segmentation, Image processing, Image classification, Performance modeling, Systems modeling, RGB color model
Automating the detection of the corn tassels during owering time is important in corn breeding. To control pollination, after a tassel is visible, the plant should be checked daily for emerging ears. The conventional methods are labor-intensive and time-consuming. In this study, we developed a technique for automatic detecting and locating corn tassel in unmanned aerial vehicle (UAV) imagery with the state-of-the art Faster Region based Convolutional Neural Network (Faster R-CNN). Each raw image was divided into 1000 x 1000 pixels sub-images, and 2000 sub-images were manually annotated for tassel locations with bounding boxes as ground-truth data. 80% of the annotated sub-images were used as training data and the remaining 20% were used for testing. The performance of the trained Faster R-CNN model was evaluated by customized evaluation criteria. The model achieved good performance on tassel detection with mean average precision of 91.78% and F1 score up to 97.98%.