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High-Throughput UAV Image-Based Method Is More Precise Than Manual Rating of Herbicide Tolerance
Hema S N Duddu1, Eric N Johnson1, Christian J Willenborg1
1Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, Canada.
Plant Phenomics (Washington, D.C.)
|December 14, 2020
Summary
Unmanned aerial vehicle (UAV) imagery and optimized soil-adjusted vegetation index (OSAVI) offer a precise alternative to traditional visual ratings for assessing herbicide crop injury. This advanced method minimizes variability and enhances accuracy in plant phenotyping.
Area of Science:
- Agricultural Science
- Remote Sensing
- Plant Science
Background:
- Traditional visual crop assessment is labor-intensive, time-consuming, and susceptible to human error.
- Unmanned aerial vehicle (UAV) imagery and vegetation indices (VI) show promise for high-throughput plant phenotyping.
- Developing objective, accurate methods for evaluating herbicide crop injury is crucial for crop breeding and management.
Purpose of the Study:
- To evaluate the accuracy and consistency of UAV imagery-based vegetation indices for estimating herbicide-induced crop injury.
- To compare the precision of UAV-based optimized soil-adjusted vegetation index (OSAVI) with traditional visual ratings.
- To determine the potential of UAV imagery as a replacement for subjective visual assessments in crop herbicide tolerance screening.
Main Methods:
- Field trials were conducted on fababean (Vicia faba L.) at the Kernen Crop Research Farm (2016-2017).
- Nine herbicide tank mixtures were applied at two rates in a randomized complete block design (RCBD) with four blocks.
- Multispectral imagery was captured one week post-application (1.2 cm GSD), with simultaneous visual ratings of growth reduction and chlorosis. Optimized soil-adjusted vegetation index (OSAVI) was calculated.
Main Results:
- UAV-based OSAVI provided significantly more precise estimations of crop injury than visual ratings across both years.
- OSAVI exhibited a low coefficient of variation (CV) of approximately 1%, compared to 18-43% for visual ratings.
- Tukey's honestly significant difference (HSD) test showed superior mean separation with OSAVI, indicating higher accuracy.
Conclusions:
- UAV-based OSAVI significantly reduces the variability associated with visual crop injury assessments.
- The UAV imagery approach offers greater precision and consistency, making it a viable alternative to traditional visual ratings.
- This technology has strong potential for efficient and accurate screening of crop varieties for herbicide tolerance.
