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Detection of Carolina Geranium (Geranium carolinianum) Growing in Competition with Strawberry Using Convolutional Neural Networks

Published online by Cambridge University Press:  04 December 2018

Shaun M. Sharpe ,
Arnold W. Schumann  and
Nathan S. Boyd
Shaun M. Sharpe
Affiliation:
Postdoctoral Associate, University of Florida, Gulf Coast Research and Education Center, Wimauma, FL, USA
Arnold W. Schumann
Affiliation:
Professor, University of Florida, Citrus Research and Education Center, Lake Alfred, FL, USA
Nathan S. Boyd*
Affiliation:
Associate Professor, University of Florida, Gulf Coast Research and Education Center, Wimauma, FL, USA
*
Author for correspondence: Nathan S. Boyd, University of Florida, Gulf Coast Research and Education Center, 14625 Count Road 672, Wimauma, FL 33598. (Email: [email protected])
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Abstract

Weed interference during crop establishment is a serious concern for Florida strawberry [Fragaria×ananassa (Weston) Duchesne ex Rozier (pro sp.) [chiloensis×virginiana]] producers. In situ remote detection for precision herbicide application reduces both the risk of crop injury and herbicide inputs. Carolina geranium (Geranium carolinianum L.) is a widespread broadleaf weed within Florida strawberry production with sensitivity to clopyralid, the only available POST broadleaf herbicide. Geranium carolinianum leaf structure is distinct from that of the strawberry plant, which makes it an ideal candidate for pattern recognition in digital images via convolutional neural networks (CNNs). The study objective was to assess the precision of three CNNs in detecting G. carolinianum. Images of G. carolinianum growing in competition with strawberry were gathered at four sites in Hillsborough County, FL. Three CNNs were compared, including object detection–based DetectNet, image classification–based VGGNet, and GoogLeNet. Two DetectNet networks were trained to detect either leaves or canopies of G. carolinianum. Image classification using GoogLeNet and VGGNet was largely unsuccessful during validation with whole images (Fscore<0.02). CNN training using cropped images increased G. carolinianum detection during validation for VGGNet (Fscore=0.77) and GoogLeNet (Fscore=0.62). The G. carolinianum leaf–trained DetectNet achieved the highest Fscore (0.94) for plant detection during validation. Leaf-based detection led to more consistent detection of G. carolinianum within the strawberry canopy and reduced recall-related errors encountered in canopy-based training. The smaller target of leaf-based DetectNet did increase false positives, but such errors can be overcome with additional training images for network desensitization training. DetectNet was the most viable CNN tested for image-based remote sensing of G. carolinianum in competition with strawberry. Future research will identify the optimal approach for in situ detection and integrate the detection technology with a precision sprayer.

Keywords

Ramon G. Leon, North Carolina State UniversityDeep-learning GPU training systemFscoreimage classificationmachine visionobject detection
Type
Research Article
Information
Weed Science , Volume 67 , Issue 2 , March 2019 , pp. 239 - 245
Copyright
© Weed Science Society of America, 2018 

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