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Detection of broadleaf weeds growing in turfgrass with convolutional neural networks
Author(s) -
Yu Jialin,
Sharpe Shaun M,
Schumann Arnold W,
Boyd Nathan S
Publication year - 2019
Publication title -
pest management science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.296
H-Index - 125
eISSN - 1526-4998
pISSN - 1526-498X
DOI - 10.1002/ps.5349
Subject(s) - cynodon dactylon , paspalum notatum , convolutional neural network , weed , weed control , agronomy , deep learning , multispectral image , computer science , artificial intelligence , biology
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 F 1 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