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

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%.

[1]  Zhe Xu,et al.  Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV , 2014, Remote. Sens..

[2]  Ismail Kavdir,et al.  Detecting corn tassels using computer vision and support vector machines , 2014, Expert Syst. Appl..

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[5]  Tristan Perez,et al.  DeepFruits: A Fruit Detection System Using Deep Neural Networks , 2016, Sensors.

[6]  Sergio Guadarrama,et al.  Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Jefersson Alex dos Santos,et al.  Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[9]  James Patrick Underwood,et al.  Deep fruit detection in orchards , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[11]  Minzan Li,et al.  Corn tassel detection based on image processing , 2011, International Workshop on Image Processing and Optical Engineering.

[12]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  K. Lamkey,et al.  MAIZE BREEDING , 2005 .

[14]  Zhiguo Cao,et al.  Fine-grained maize tassel trait characterization with multi-view representations , 2015, Comput. Electron. Agric..

[15]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[16]  Nithya Rajan,et al.  Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research , 2016, PloS one.

[17]  Roland Siegwart,et al.  weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming , 2017, IEEE Robotics and Automation Letters.

[18]  Yuntao Ma,et al.  Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN , 2020, Remote. Sens..

[19]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[20]  Zhiguo Cao,et al.  TasselNet: counting maize tassels in the wild via local counts regression network , 2017, Plant Methods.

[21]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[22]  Cyrill Stachniss,et al.  REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS , 2017 .

[23]  Sang Cheol Kim,et al.  A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition , 2017, Sensors.