Paper Info
Title
Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning
Abstract
The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean lines were visually classified into three classes of injury, i.e., tolerant, moderate, and susceptible to off-target dicamba. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. The imagery-based classification model can be implemented in a breeding program to effectively differentiate phenotypic dicamba response and identify soybean lines with tolerance to off-target dicamba damage.
Year
DOI
Venue
2022
10.3390/rs14071618
REMOTE SENSING
Keywords
DocType
Volume
soybean, dicamba, RGB, UAV, machine learning
Journal
14
Issue
ISSN
Citations 
7
2072-4292
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Caio Canella Vieira100.34
Shagor Sarkar200.34
Fengkai Tian300.34
Jing Zhou432754.75
Diego Jarquin500.34
Henry T. Nguyen600.34
Jianfeng Zhou765.49
Pengyin Chen851.18