Detection of broadleaf weeds growing in turfgrass with convolutional neural networks

BACKGROUND Weed infestations reduce turfgrass aesthetics and uniformity. Postemergence (POST) herbicides are applied uniformly on turfgrass, hence areas without weeds are also sprayed. Deep learning, particularly the architecture of convolutional neural network (CNN), is a state‐of‐art approach to r...

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Bibliographic Details
Published in:Pest management science 2019-08, Vol.75 (8), p.2211-2218
Main Authors: Yu, Jialin, Sharpe, Shaun M, Schumann, Arnold W, Boyd, Nathan S
Format: Article
Language:English
Subjects:
Artificial neural networks
Decay rate
Deep learning
Herbicides
Machine learning
machine vision
Neural networks
Object recognition
Organic chemistry
Paspalum notatum
precision herbicide application
Sprays
Turfgrasses
Weed control
Weeds
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Summary:BACKGROUND Weed infestations reduce turfgrass aesthetics and uniformity. Postemergence (POST) herbicides are applied uniformly on turfgrass, hence areas without weeds are also sprayed. Deep learning, particularly the architecture of convolutional neural network (CNN), is a state‐of‐art approach to recognition of images and objects. In this paper, we report deep learning CNN (DL‐CNN) models that are remarkably accurate at detection of broadleaf weeds in turfgrasses. RESULTS VGGNet was the best model for detection of various broadleaf weeds growing in dormant bermudagrass [Cynodon dactylon (L.)] and DetectNet was the best model for detection of cutleaf evening‐primrose (Oenothera laciniata Hill) in bahiagrass (Paspalum notatum Flugge) when the learning rate policy was exponential decay. These models achieved high F1 scores (>0.99) and overall accuracy (>0.99), with recall values of 1.00 in the testing datasets. CONCLUSION The results of the present research demonstrate the potential for detection of broadleaf weed using DL‐CNN models for detection of broadleaf weeds in turfgrass systems. Further research is required to evaluate weed control in field conditions using these models for in situ video input in conjunction with a smart sprayer. © 2019 Society of Chemical Industry This research demonstrated the feasibility of using deep learning convolutional neural network (DL‐CNN) for detection of weeds in turfgrasses. The DL‐CNN models will be evaluated for in situ video input in a smart‐sprayer.
ISSN:1526-498X
1526-4998
DOI:10.1002/ps.5349