var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"title\":\"Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches\",\"type\":\"book\",\"year\":\"2022\",\"source\":\"Communications in Computer and Information Science\",\"keywords\":\"Convolutional neural networks,Deep learning,Smart farming\",\"volume\":\"1643 CCIS\",\"id\":\"00db5c55-3459-3cc0-88a9-a9123168643a\",\"created\":\"2023-01-27T11:35:14.242Z\",\"file_attached\":false,\"profile_id\":\"8dfcede1-2f4c-3600-8b67-8b0c1f334b84\",\"last_modified\":\"2023-01-27T11:35:14.242Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":false,\"hidden\":false,\"private_publication\":\"true\",\"abstract\":\"Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).\",\"bibtype\":\"book\",\"author\":\"Raki, H. and González-Vergara, J. and Aalaila, Y. and Elhamdi, M. and Bamansour, S. and Guachi-Guachi, L. and Peluffo-Ordoñez, D.H.\",\"doi\":\"10.1007/978-3-031-19647-8_3\",\"bibtex\":\"@book{\\n title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},\\n type = {book},\\n year = {2022},\\n source = {Communications in Computer and Information Science},\\n keywords = {Convolutional neural networks,Deep learning,Smart farming},\\n volume = {1643 CCIS},\\n id = {00db5c55-3459-3cc0-88a9-a9123168643a},\\n created = {2023-01-27T11:35:14.242Z},\\n file_attached = {false},\\n profile_id = {8dfcede1-2f4c-3600-8b67-8b0c1f334b84},\\n last_modified = {2023-01-27T11:35:14.242Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {true},\\n abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},\\n bibtype = {book},\\n author = {Raki, H. and González-Vergara, J. and Aalaila, Y. and Elhamdi, M. and Bamansour, S. and Guachi-Guachi, L. and Peluffo-Ordoñez, D.H.},\\n doi = {10.1007/978-3-031-19647-8_3}\\n}\",\"author_short\":[\"Raki,  H.\",\"González-Vergara,  J.\",\"Aalaila,  Y.\",\"Elhamdi,  M.\",\"Bamansour,  S.\",\"Guachi-Guachi,  L.\",\"Peluffo-Ordoñez,  D.\"],\"biburl\":\"https://bibbase.org/service/mendeley/8dfcede1-2f4c-3600-8b67-8b0c1f334b84\",\"bibbaseid\":\"raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Convolutional neural networks\",\"Deep learning\",\"Smart farming\"],\"metadata\":{\"authorlinks\":{\"raki,  h\":\"https://www.sdas-group.com/members/Sami-Bamansour/\"}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"owner\":\"raki,  h\",\"authorlinks\":{\"raki,  h\":\"https://www.sdas-group.com/members/Sami-Bamansour/\"}},\n      ownerID: \"Km5kmnL2hbCmHcYbG\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,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\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/8dfcede1-2f4c-3600-8b67-8b0c1f334b84?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/8dfcede1-2f4c-3600-8b67-8b0c1f334b84?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/8dfcede1-2f4c-3600-8b67-8b0c1f334b84?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.sdas-group.com/members/Sami-Bamansour/\">Raki,  H.</a>; González-Vergara,  J.; Aalaila,  Y.; Elhamdi,  M.; Bamansour,  S.; Guachi-Guachi,  L.;  and Peluffo-Ordoñez,  D.\n</span>\n\n  \n\n</span>\n\n  Volume 1643 CCIS  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-031-19647-8_3\"\n     onclick=\"log_download('raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022', 'http://doi.org/10.1007/978-3-031-19647-8_3', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@book{\n title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},\n type = {book},\n year = {2022},\n source = {Communications in Computer and Information Science},\n keywords = {Convolutional neural networks,Deep learning,Smart farming},\n volume = {1643 CCIS},\n id = {00db5c55-3459-3cc0-88a9-a9123168643a},\n created = {2023-01-27T11:35:14.242Z},\n file_attached = {false},\n profile_id = {8dfcede1-2f4c-3600-8b67-8b0c1f334b84},\n last_modified = {2023-01-27T11:35:14.242Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},\n bibtype = {book},\n author = {Raki, H. and González-Vergara, J. and Aalaila, Y. and Elhamdi, M. and Bamansour, S. and Guachi-Guachi, L. and Peluffo-Ordoñez, D.H.},\n doi = {10.1007/978-3-031-19647-8_3}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);