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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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%.

Original languageEnglish (US)
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
EditorsJ. Alex Thomasson, Alfonso F. Torres-Rua
PublisherSPIE
ISBN (Electronic)9781510636057
DOIs
StatePublished - 2020
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V 2020 - Virtual, Online, United States
Duration: Apr 27 2020May 8 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11414
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V 2020
CountryUnited States
CityVirtual, Online
Period4/27/205/8/20

Keywords

  • CNN
  • Faster R-CNN
  • Flowering
  • Object detection
  • Phenotyping

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Faster R-CNN-based deep learning for locating corn tassels in UAV imagery'. Together they form a unique fingerprint.

  • Cite this

    Al-Zadjali, A., Shi, Y., Scott, S., Deogun, J. S., & Schnable, J. (2020). Faster R-CNN-based deep learning for locating corn tassels in UAV imagery. In J. A. Thomasson, & A. F. Torres-Rua (Eds.), Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V [1141406] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11414). SPIE. https://doi.org/10.1117/12.2560596