Computer vision technology for identification of snap bean crops using systemic rhodamine B

Wen-Hao Su, Steven A. Fennimore, David C. Slaughter

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

3 Scopus citations

Abstract

The paper describes the development of a novel seed treatment developed to create a machine-readable vegetable crop plant that together with a compatible robotic computer vision system that together can address current challenges that traditional computer vision and machine learning approaches face when attempting to detect and identify vegetable crop plants growing in fields with high weed densities and significant levels of foliage occlusion. A novel computer vision system was designed with a custom illumination system customized to excite the fluorescence properties of systemic Rhodamine B and image them in vivo. Study results, using snap beans at the model crop, show that the system can detect and allow visualization of the Rhodamine dye internal to the crop system in snap bean stems from stages of first leaf to multiple leaves. The research demonstrates that a Crop Signaling approach, using Rhodamine B can be used by a computer vision system to automatically discriminate weeds from snap bean crops.

Original languageEnglish (US)
Title of host publicationASABE Annual International Meeting
DOIs
StatePublished - Jul 10 2019
Externally publishedYes
Event2019 ASABE Annual International Meeting - Boston, United States
Duration: Jul 7 2019Jul 10 2019

Conference

Conference2019 ASABE Annual International Meeting
CountryUnited States
CityBoston
Period7/7/197/10/19

Keywords

  • Fluorescence imaging
  • Rhodamine B
  • Specialty crop
  • Systemic marker
  • Weed control

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