Faster R-CNN-based deep learning for locating corn tassels in UAV imagery

Aziza Al-Zadjali; Yeyin Shi; Stephen Scott; Jitender S. Deogun; James Schnable

doi:10.1117/12.2560596

23 April 2020 Faster R-CNN-based deep learning for locating corn tassels in UAV imagery

Aziza Al-Zadjali, Yeyin Shi, Stephen Scott, Jitender S. Deogun, James Schnable

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 1141406 (2020) https://doi.org/10.1117/12.2560596
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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%.

Conference Presentation

Citation Download Citation

Aziza Al-Zadjali, Yeyin Shi, Stephen Scott, Jitender S. Deogun, and James Schnable "Faster R-CNN-based deep learning for locating corn tassels in UAV imagery", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 1141406 (23 April 2020); https://doi.org/10.1117/12.2560596

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