Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

Zhuang, Jiayao; Li, Xuehan; Bagavathiannan, Muthukumar; Xiao, Jin; Yang, Jie; Meng, Weibin; Li, Tao; Li, Lanxi; Wang, Yundi; Chen, Yong; Yu, Jialin

doi:10.1002/ps.6656

Cited by 40 publications

(30 citation statements)

References 58 publications

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“…Finally, in 2008, Li, L. introduced a collection digital data device for dynamic monitoring; the device includes an infrared image sensor, an ARM and DSP-based data processing module, a GPS system, and a data link ( Li et al., 2004 , 2008 ; Yuan et al., 2006 ). In 2021, the author participated in publications such as “Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat” ( Zhuang et al., 2021 ). Therefore, the author with the most publications is an expert focussed on technological development solutions for digital agriculture.…”

Section: Resultsmentioning

confidence: 99%

A bibliometric and thematic approach to agriculture 4.0

Mühl

Oliveira

2022

Heliyon

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Section: Resultsmentioning

confidence: 99%

A bibliometric and thematic approach to agriculture 4.0

Mühl

Oliveira

2022

Heliyon

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“…The authors found that the ratios of positive and negative images in the training dataset affected the performances of the neural networks for weed detection. Recently, Zhuang et al 51 reported that increasing training image sizes from 200 × 200 pixels to 400 × 400 pixels increased the F 1 scores of DenseNet and ResNet, but generally decreased those of AlexNet and VGGNet for the detection of broadleaf weed seedlings growing in wheat (Triticum aestivum L.). However, the authors noted that increasing training image numbers increased classification accuracy, diminishing the differences in training image sizes.…”

Section: Discussionmentioning

confidence: 99%

A deep learning‐based method for classification, detection, and localization of weeds in turfgrass

Xiao

Bagavathiannan

McCullough

et al. 2022

Pest Management Science

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BACKGROUND Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate the weeds on the input images. The objectives of this research were to: (i) investigate the feasibility of training deep learning models using grid cells (subimages) to detect the location of weeds on the image by identifying whether or not the grid cells contain weeds; and (ii) evaluate DenseNet, EfficientNetV2, ResNet, RegNet and VGGNet to detect and discriminate multiple weed species growing in turfgrass (multi‐classifier) and detect and discriminate weeds (regardless of weed species) and turfgrass (two‐classifier). RESULTS The VGGNet multi‐classifier exhibited an F1 score of 0.950 when used to detect common dandelion and achieved high F1 scores of ≥0.983 to detect and discriminate the subimages containing dallisgrass, purple nutsedge and white clover growing in bermudagrass turf. DenseNet, EfficientNetV2 and RegNet multi‐classifiers exhibited high F1 scores of ≥0.984 for detecting dallisgrass and purple nutsedge. Among the evaluated neural networks, EfficientNetV2 two‐classifier exhibited the highest F1 scores (≥0.981) for exclusively detecting and discriminating subimages containing weeds and turfgrass. CONCLUSION The proposed method can accurately identify the grid cells containing weeds and thus precisely locate the weeds on the input images. Overall, we conclude that the proposed method can be used in the machine vision subsystem of smart sprayers to locate weeds and make the decision for precision spraying herbicides onto the individual map cells. © 2022 Society of Chemical Industry.

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“…In the event of irregularly shaped plants or patches of plants, multiple bounding boxes were drawn to encompass the entirety of the plant features. Labeling partial sections of irregularly shaped plants has been shown to be beneficial to object detectors (Sharpe et al 2018(Sharpe et al , 2020aZhuang et al 2022), so these irregular features were not ignored. In any given image, both plant species could be present, so they were labeled accordingly.…”

Section: Image Processing and Data Annotationmentioning

confidence: 99%

Use of open-source object detection algorithms to detect Palmer amaranth (Amaranthus palmeri) in soybean

et al. 2022

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Site-specific weed management using open-sourced object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth (Amaranthus palmeri S. Watson) in soybean [Glycine max (L.) Merr.]. The objectives were to 1) develop an annotated image database of A. palmeri and soybean to fine-tune object detection algorithms, 2) compare effectiveness of multiple open-sourced algorithms in detecting A. palmeri, and 3) evaluate the relationship between A. palmeri growth features and A. palmeri detection ability. Soybean field sites were established in Manhattan, KS and Gypsum, KS with natural populations of A. palmeri. A total of 1108 and 392 images were taken aerially and at ground level, respectively, between May 27 and July 27, 2021. After image annotation, a total of 4492 images were selected. Annotated images were used to fine tune open-source Faster regional convolutional (Faster R-CNN) and Single Shot Detector (SSD) algorithms using a Resnet backbone, as well as the You Only Look Once (YOLO) series algorithms. Results demonstrated that YOLO version 5 achieved the highest mean average precision score of 0.77. For both A. palmeri and soybean detections within this algorithm, the highest F1 score was 0.72 when using a confidence threshold of 0.298. A lower confidence threshold of 0.15 increased the likelihood of species detection, but also increased the likelihood of false positive detections. The trained YOLOv5 dataset was used to identify A. palmeri in a dataset paired with measured growth features. Linear regression models predicted that as A. palmeri densities increased and as A. palmeri height increased, precision, recall, and F1 scores of algorithms decreased. We conclude that open-sourced algorithms such as YOLOv5 show great potential in detecting A. palmeri in soybean cropping systems.

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Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

Cited by 40 publications

References 58 publications

A bibliometric and thematic approach to agriculture 4.0

A bibliometric and thematic approach to agriculture 4.0

A deep learning‐based method for classification, detection, and localization of weeds in turfgrass

Use of open-source object detection algorithms to detect Palmer amaranth (Amaranthus palmeri) in soybean

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