Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture

Ciaran Robb*, Andy Hardy, John H. Doonan, Jason Brook

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)
111 Downloads (Pure)


We present an image processing method for accurately segmenting crop plots from Unmanned Aerial System imagery (UAS). The use of UAS for agricultural monitoring has increased significantly, emerging as a potentially cost effective alternative to manned aerial surveys and field work for remotely assessing crop state. The accurate segmentation of small densely-packed crop plots from UAS imagery over extensive areas is an important component of this monitoring activity in order to assess the state of different varieties and treatment regimes in a timely and cost-effective manner. Despite its importance, a reliable crop plot segmentation approach eludes us, with best efforts being relying on significant manual parameterization. The segmentation method developed uses a combination of edge detection and Hough line detection to establish the boundaries of each plot with pixel/point based metrics calculated for each plot segment. We show that with limited parameterization, segmentation of crop plots consistently over 89% accuracy are possible on different crop types and conditions. This is comparable to results obtained from rice paddies where the plant material in plots is sharply contrasted with the water, and represents a considerable improvement over previous methods for typical dry land crops.

Original languageEnglish
Article number591886
Number of pages13
JournalFrontiers in Plant Science
Publication statusPublished - 09 Dec 2020


  • crop plot
  • edge-detection
  • Hough-transform
  • segmentation
  • structure-from-motion
  • UAS


Dive into the research topics of 'Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture'. Together they form a unique fingerprint.

Cite this