Liquid Exception: Failed to parse BibTeX on value "pdf""

[uverma]

I am unable to deploy the website. The Github Action fails with the error "Liquid Exception: Failed to parse BibTeX on value "pdf""

The source code is available on https://github.com/uverma/uverma.github.io

Run yes | bin/deploy --verbose --src source --deploy master

• shift
  Deploying...
• [[ 4 > 0 ]]
• key=--src
• case $key in
• SRC_BRANCH=source
• shift
• shift
• [[ 2 > 0 ]]
• key=--deploy
• case $key in
• DEPLOY_BRANCH=master
• shift
• shift
• [[ 0 > 0 ]]
• set -e
• echo Deploying...
• echo 'Source branch: source'
• echo 'Deploy branch: master'
  Source branch: source
  Deploy branch: master
• read -r -p 'Do you want to proceed? [y/N] ' response
• [[ ! y =~ ^([yY][eE][sS]|[yY])+$ ]]
• git diff-index --quiet HEAD --
  ++ git ls-files --exclude-standard --others
• test -z ''
  ++ git branch
  ++ grep source
  ++ tr ' ' '\n'
  ++ tail -1
• '[' source ']'
• git checkout source
  Already on 'source'
  Your branch is up to date with 'origin/source'.
  ++ git branch
  ++ grep master
• '[' ']'
• git checkout -b master
  Switched to a new branch 'master'
• bundle exec jekyll build
  Configuration file: /home/runner/work/uverma.github.io/uverma.github.io/_config.yml
  Source: /home/runner/work/uverma.github.io/uverma.github.io
  Destination: /home/runner/work/uverma.github.io/uverma.github.io/_site
  Incremental build: disabled. Enable with --incremental
  Generating...
  AutoPages: Disabled/Not configured in site.config.
  Pagination: Complete, processed 1 pagination page(s)
  E, [2021-04-11 08:36:51#2513] ERROR -- : Failed to parse BibTeX on value "pdf" (NAME) [#<BibTeX::Bibliography data=[1]>, "@", #<BibTeX::Entry >, {:title=>["Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning"], :author=>["Shorewala, Shantam and Ashfaque, Armaan and Sidharth, R and Verma, Ujjwal"], :journal=>["IEEE Access"], :volume=>["9"], :pages=>["27971--27986"], :year=>["2021"], :publisher=>["IEEE"], :abstract=>["Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data."]}]
  Liquid Exception: Failed to parse BibTeX on value "pdf" (NAME) [#<BibTeX::Bibliography data=[1]>, "@", #<BibTeX::Entry >, {:title=>["Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning"], :author=>["Shorewala, Shantam and Ashfaque, Armaan and Sidharth, R and Verma, Ujjwal"], :journal=>["IEEE Access"], :volume=>["9"], :pages=>["27971--27986"], :year=>["2021"], :publisher=>["IEEE"], :abstract=>["Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data."]}] in /home/runner/work/uverma.github.io/uverma.github.io/_layouts/about.html
  ------------------------------------------------
  Jekyll 4.2.0 Please append --trace to the build command
  for any additional information or backtrace.
  ------------------------------------------------
  bibtex.y:138:in `on_error': Failed to parse BibTeX on value "pdf" (NAME) [#<BibTeX::Bibliography data=[1]>, "@", #<BibTeX::Entry >, {:title=>["Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning"], :author=>["Shorewala, Shantam and Ashfaque, Armaan and Sidharth, R and Verma, Ujjwal"], :journal=>["IEEE Access"], :volume=>["9"], :pages=>["27971--27986"], :year=>["2021"], :publisher=>["IEEE"], :abstract=>["Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data."]}] (BibTeX::ParseError)

[alshedivat]

Looks like one of your bibtex entries is incorrectly formatted. I believe you need to add a comma at the end of line 42. Hope this helps.