{"id":164549,"date":"2022-06-01T09:54:00","date_gmt":"2022-06-01T08:54:00","guid":{"rendered":"https:\/\/multi.datascientest.com\/?p=164549"},"modified":"2023-10-30T18:12:54","modified_gmt":"2023-10-30T17:12:54","slug":"mushroom-recognition","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/mushroom-recognition","title":{"rendered":"Mushroom recognition : Using Computer Vision in Machine Learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"164549\" class=\"elementor elementor-164549\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ea872a0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ea872a0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-94c3575\" data-id=\"94c3575\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a99fe8b elementor-widget elementor-widget-heading\" data-id=\"a99fe8b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">Context<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eaf7fd2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eaf7fd2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-02c7e1b\" data-id=\"02c7e1b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-907b8b3 elementor-widget elementor-widget-text-editor\" data-id=\"907b8b3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p id=\"4998\" class=\"pw-post-body-paragraph lj lk iw ll b lm ln lo lp lq lr ls lt lu lv lw lx ly lz ma mb mc md me mf mg ip fy\" data-selectable-paragraph=\"\">One of the most widely used and oldest species identification techniques is <span style=\"color: #5e36f3\"><strong>\u201cmorphological identification\u201d<\/strong><\/span>, which identifies individuals by their anatomical characteristics. However, this technique has the disadvantage of not being always accurate since it depends on the observation and the identification protocol of the person who performs it.<\/p><p id=\"8588\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">An alternative to this identification technique could be computer vision algorithms. These algorithms allow us to identify <strong><span style=\"color: #5e36f3\">the characteristics of an image in a fast and accurate way<\/span><\/strong>, so the identification of a species can be made only through a photo, making it more portable.<\/p><p id=\"58ed\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">In the following, we try to identify different mushroom images with the help of computer vision. This work was realized together with my promotion colleague Cyril Vandenberghe and supervised by Adrien Ruimy. It corresponds to the <a class=\"au mm\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\" target=\"_blank\" rel=\"noopener ugc nofollow\"><strong><span style=\"color: #5e36f3\">DataScientest data scientist course<\/span><\/strong><\/a> final project. The development took a hundred hours and allowed us to utilize our newly acquired knowledge in a practical application.<\/p><p id=\"6e6d\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">Project Authors : <a class=\"au mm\" href=\"https:\/\/www.linkedin.com\/in\/karina-castillo-perez\/\" target=\"_blank\" rel=\"noopener ugc nofollow\"><strong><span style=\"color: #5e36f3\">Karina Castillo<\/span><\/strong><\/a> &amp; <a class=\"au mm\" href=\"https:\/\/www.linkedin.com\/in\/vdbcyril\/\" target=\"_blank\" rel=\"noopener ugc nofollow\"><strong><span style=\"color: #5e36f3\">Cyril Vandenberghe<\/span><\/strong><\/a>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-71c5107 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"71c5107\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-edcc279\" data-id=\"edcc279\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-959efc7 elementor-widget elementor-widget-heading\" data-id=\"959efc7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The project<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9e4b1de elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9e4b1de\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-291ab5a\" data-id=\"291ab5a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ff3da24 elementor-widget elementor-widget-heading\" data-id=\"ff3da24\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Data source<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a4379c6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a4379c6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-40f5b16\" data-id=\"40f5b16\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ff6a8fb elementor-widget elementor-widget-text-editor\" data-id=\"ff6a8fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"78b9\" class=\"pw-post-body-paragraph lj lk iw ll b lm ln lo lp lq lr ls lt lu lv lw lx ly lz ma mb mc md me mf mg ip fy\" data-selectable-paragraph=\"\">For this project, we used data from <span style=\"color: #5e36f3\"><strong><a class=\"au mm\" style=\"color: #5e36f3\" href=\"http:\/\/mushroomobserver.org\/\" target=\"_blank\" rel=\"noopener ugc nofollow\">http:\/\/mushroomobserver.org<\/a><\/strong><\/span>, a website dedicated to collect and identify mushroom images: Users upload images, and the community identifies them. Currently, it counts over 10,000 registered users.<\/p><p id=\"de0d\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">During our initial bibliographical research, we found that a previous project hosted on Github, <span style=\"color: #5e36f3\"><strong>\u00ab<\/strong> <a class=\"au mm\" style=\"color: #5e36f3\" href=\"https:\/\/github.com\/bechtle\/mushroomobser-dataset\" target=\"_blank\" rel=\"noopener ugc nofollow\"><strong class=\"ll ix\">mushroomobser-dataset<\/strong><\/a> \u00bb<\/span> had already explored this site to create a dataset of images dating from 2006 to 2016. Additionally, 12 JSON files contain additional information about each image.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1779cdf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1779cdf\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4d30a9e\" data-id=\"4d30a9e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fe225db elementor-widget elementor-widget-image\" data-id=\"fe225db\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-v2pJ4VymwVI-F55xa1b2rA.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 1. Example of a JSON entry for one image. (image by Github: bechtle).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5582783 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5582783\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-68ee860\" data-id=\"68ee860\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2955c57 elementor-widget elementor-widget-heading\" data-id=\"2955c57\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Data exploration<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8487d95 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8487d95\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c435438\" data-id=\"c435438\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-555485c elementor-widget elementor-widget-text-editor\" data-id=\"555485c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The first step in processing our data was to create a data frame with information about the images and <span style=\"color: #5e36f3\"><strong class=\"ll ix\">taxonomy<\/strong> <\/span>of the mushrooms. In biology, a taxonomy is a way of defining hierarchical groups based on common characteristics, as shown in the figure below :<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9079ca4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9079ca4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a6203d8\" data-id=\"a6203d8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-14ee8a5 elementor-widget elementor-widget-image\" data-id=\"14ee8a5\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-QAW4Cc-KLRAr6rIb0Yix7w.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 2. Taxonomy organization (Image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-17181e3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"17181e3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-287c5d0\" data-id=\"287c5d0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8574072 elementor-widget elementor-widget-text-editor\" data-id=\"8574072\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In the table 1, the `<em class=\"np\">rank<\/em>` column indicates the last taxonomic level reached in the mushroom identification in our data frame. Thus, if an entry has the value `<span style=\"color: #5e36f3\"><strong><em class=\"np\">KINGDOM<\/em><\/strong><\/span>` in the `<em class=\"np\">rank<\/em>` column, the `<em class=\"np\">label<\/em>` column value will be the most general identification, e.g., `<em class=\"np\">Fungi<\/em>.` The identified images can therefore belong to any mushroom.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f4bde87 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f4bde87\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a83c102\" data-id=\"a83c102\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1410982 elementor-widget elementor-widget-image\" data-id=\"1410982\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-gy4Ol0Ho6HvQ6A4j0zYYNA.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Table 1. Example of values in the data frame when \"rank\" = \"KINGDOM\".<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-be406e7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"be406e7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d3cf907\" data-id=\"d3cf907\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-33d7e1e elementor-widget elementor-widget-text-editor\" data-id=\"33d7e1e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In contrast, if the `<em class=\"np\">rank<\/em>` column is `<span style=\"color: #5e36f3\"><strong><em class=\"np\">SPECIES<\/em><\/strong><\/span>` (a more specific taxon), we have a more detailed description of the images (table 2).<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-da617d2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"da617d2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a590d69\" data-id=\"a590d69\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-28d4b3e elementor-widget elementor-widget-image\" data-id=\"28d4b3e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-1c5S6Y_YwSZhVHCBHUhR4g.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Table 2. Example of values in the data frame when \"rank\" = \"SPECIES\".<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9e01abf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9e01abf\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bcd02cd\" data-id=\"bcd02cd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-824a5c0 elementor-widget elementor-widget-text-editor\" data-id=\"824a5c0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Therefore, images with `<em class=\"np\"><span style=\"color: #5e36f3\"><strong>KINGDOM<\/strong><\/span>`<\/em>, `<strong><span style=\"color: #5e36f3\"><em class=\"np\">PHYLUM<\/em><\/span><\/strong>`, `<strong><span style=\"color: #5e36f3\"><em class=\"np\">CLASS<\/em><\/span><\/strong>` and `<strong><span style=\"color: #5e36f3\"><em class=\"np\">ORDER<\/em><\/span><\/strong>` values in the `<em class=\"np\">rank<\/em>` column were discarded, as they are too general and non-informative. For the rest of the project, we will only work with data belonging to the taxa <em class=\"np\">SPECIES<\/em>, <em class=\"np\">GENUS<\/em>, and <em class=\"np\">FAMILY<\/em> as shown in the figure below :<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2b616a2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2b616a2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-31d893a\" data-id=\"31d893a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-72c4feb elementor-widget elementor-widget-image\" data-id=\"72c4feb\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-ifuPCIhGO7v68XxXopolUA.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 3. Selected taxa (Image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0fad69e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0fad69e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4ac25a7\" data-id=\"4ac25a7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-29ecba9 elementor-widget elementor-widget-heading\" data-id=\"29ecba9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Data Visualisation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b6e7c70 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b6e7c70\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-dcf0c0c\" data-id=\"dcf0c0c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c6ca831 elementor-widget elementor-widget-text-editor\" data-id=\"c6ca831\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"f6e7\" class=\"pw-post-body-paragraph lj lk iw ll b lm ln lo lp lq lr ls lt lu lv lw lx ly lz ma mb mc md me mf mg ip fy\" data-selectable-paragraph=\"\">Having a large number of images is essential for successfully developing a <span style=\"color: #5e36f3\"><strong>computer vision<\/strong><\/span> project. In this project, we need labels (i.e., values in the `<em class=\"np\">label<\/em>` column) to have a large number of images to represent it for each taxon.<\/p><p id=\"04e9\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">The image below (fig. 4) is a visualization of the distribution of the number of images in the taxa. Each of the four panels shows the number of labels <strong><span style=\"color: #5e36f3\">(y-axis)<\/span><\/strong> for a given taxon <strong><span style=\"color: #5e36f3\">(x-axis)<\/span><\/strong> represented by 1 to 10 images, 11 to 100 images, 101 to 1000 images, and over 1000 images.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e3653c0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e3653c0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-38cd901\" data-id=\"38cd901\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0a09bcd elementor-widget elementor-widget-image\" data-id=\"0a09bcd\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-i8SP6yWXRGAW2J1kivj_mg.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">FIg. 4. Number of labels per taxon by range. (image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c693c95 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c693c95\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-78a4d82\" data-id=\"78a4d82\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a19534b elementor-widget elementor-widget-text-editor\" data-id=\"a19534b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"dbe0\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">The species taxon is the ideal one because it is the most specific one. However, the number of labels represented by only <span style=\"color: #5e36f3\"><strong>1 to 10 images corresponds to 75% of the total number of species<\/strong><\/span>. In the interval [101\u20131000], only seven species have more than 500 images, and only one species has more than 1000 images.<\/p><p id=\"3a67\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">In contrast, in the panel for categories with more than 1000 images, we observe the genus and family taxa with about 40 labels each, making them good targets. Taxonomically, family is the least specific level, so we would expect more heterogeneity in images if we pick this level. Using genus seems then a good compromise between the taxonomic level and the number of images available.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3aed248 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3aed248\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-21c78f0\" data-id=\"21c78f0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0a9413f elementor-widget elementor-widget-heading\" data-id=\"0a9413f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Final Dataset : Genus Taxa<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0973505 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0973505\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3857773\" data-id=\"3857773\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f966295 elementor-widget elementor-widget-text-editor\" data-id=\"f966295\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The bar chart below shows the number of images for 41 selected genus (Fig. 5). We decided to limit ourselves to the 15 highlighted genera containing ~1,000 images to reduce computation time and avoid a biased representation. With this, we have a total of 18,412 images available for the classification\/modeling part of our project.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bb3a18d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bb3a18d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-96c0db1\" data-id=\"96c0db1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f96a4b8 elementor-widget elementor-widget-image\" data-id=\"f96a4b8\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-uv8wrQ30Blxf8D1YdRJPwA.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 5. Histogram showing the distribution of images in the 41 genus containing more than 1000 images and the 15 genus selected for the project analyses (in the red square). (Image by author)<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bb172dc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bb172dc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-150c0c8\" data-id=\"150c0c8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-523a5c7 elementor-widget elementor-widget-heading\" data-id=\"523a5c7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Classification using Neural Networks<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f2a4ae7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f2a4ae7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-55e3ec2\" data-id=\"55e3ec2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-01c8277 elementor-widget elementor-widget-heading\" data-id=\"01c8277\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Architectures<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7f457d9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7f457d9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-276347d\" data-id=\"276347d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0b9b76 elementor-widget elementor-widget-text-editor\" data-id=\"c0b9b76\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>As mentioned before, this project aims to identify mushroom images, which is a typical image classification problem that is usually addressed by <span style=\"color: #5e36f3\"><strong>deep learning methods<\/strong><\/span>. To achieve our goal, we tested three models, detailed below.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a04aae9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a04aae9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-015977e\" data-id=\"015977e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-17b8fb8 elementor-widget elementor-widget-heading\" data-id=\"17b8fb8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">- LeNet<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7c6ea5c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7c6ea5c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-40ef7a8\" data-id=\"40ef7a8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7d01f84 elementor-widget elementor-widget-text-editor\" data-id=\"7d01f84\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"54ff\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">First, we tested our data on a <span style=\"color: #5e36f3\"><strong>CNN model<\/strong> <\/span>with a LeNet architecture (Y. LeCun <em class=\"np\">et a<\/em>l., 1998) that contains layers adapted for image processing. We tested this model with two convolutional layers.<\/p><p id=\"b803\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">With a value of 0.98% accuracy, the basic model does not seem suitable for our goal, so we did not pursue this model further.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2c55452 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2c55452\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-76e46f2\" data-id=\"76e46f2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6a4984c elementor-widget elementor-widget-heading\" data-id=\"6a4984c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">- VGG16<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f73a1f5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f73a1f5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4df3698\" data-id=\"4df3698\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d21ac1c elementor-widget elementor-widget-text-editor\" data-id=\"d21ac1c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"c6b0\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">Next, we used the <span style=\"color: #5e36f3\"><strong>VGG16<\/strong><\/span> model (Simonyan and Zisserman, 2015), an image classification model with a Transfer Learning approach.<\/p><p id=\"3eea\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">In the first step, our model had one classification layer with a Sequential model, two neural layers <strong><span style=\"color: #5e36f3\">(Dense)<\/span><\/strong>, and two regularization layers <strong><span style=\"color: #5e36f3\">(Dropout)<\/span><\/strong>.<\/p><p id=\"0242\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">The results obtained were encouraging, with an accuracy of about 70%, but the model suffered from overfitting. To counter this, we added a Dense layer and increased the Dropout value. The figure 6 shows the final architecture of the model.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7f7108d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7f7108d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3b99a74\" data-id=\"3b99a74\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0f4c1d3 elementor-widget elementor-widget-image\" data-id=\"0f4c1d3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-ccuLJoXZLE7ySYxfB4hGjQ.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 6. Final VGG16 Architecture (image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ffede6f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ffede6f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4febd30\" data-id=\"4febd30\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-30fd34a elementor-widget elementor-widget-text-editor\" data-id=\"30fd34a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Additionally, we used the <span style=\"color: #5e36f3\"><strong>EarlyStopping<\/strong> <\/span>and <strong><span style=\"color: #5e36f3\">ReduceLROnPlateau<\/span><\/strong> callbacks from the Keras package with patience values of 5 and 3 respectively. With this, we achieved an accuracy value of 75.5% without overfitting.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0bb1023 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0bb1023\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-927ea76\" data-id=\"927ea76\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3ed7748 elementor-widget elementor-widget-heading\" data-id=\"3ed7748\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">- EfficientNetB1<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bedf1c2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bedf1c2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a9f8db3\" data-id=\"a9f8db3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6b3f0e2 elementor-widget elementor-widget-text-editor\" data-id=\"6b3f0e2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The third image classification method we used in this project was also a <span style=\"color: #5e36f3\"><strong>Transfer Learning<\/strong><\/span> approach with the <strong><span style=\"color: #5e36f3\">Tensorflow EfficientNet model<\/span> <\/strong>(Tan and Le, 2019). We took inspiration from our previous model (VGG16) for the classification architecture we implemented, as we had positive results (fig. 7). We also used the callbacks <strong><span style=\"color: #5e36f3\">EarlyStopping<\/span> <\/strong>and <strong><span style=\"color: #5e36f3\">ReduceLROnPlateau<\/span> <\/strong>with the same values.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6852f69 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6852f69\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3f868d9\" data-id=\"3f868d9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9a579ad elementor-widget elementor-widget-image\" data-id=\"9a579ad\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-WR5X52mYnAYWvy5vS1Odag.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 7. Fnal EfficientNetB1 architecture (image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d63802f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d63802f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4718521\" data-id=\"4718521\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5fe4076 elementor-widget elementor-widget-text-editor\" data-id=\"5fe4076\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"056e\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">The first results show an accuracy value of about 80%, better than with VGG16, but still showing overfitting, so we increased the value of the Dropout layers and finally got a score of 79% without overfitting.<\/p><p id=\"671d\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">Here is a display of the <span style=\"color: #5e36f3\"><strong>model\u2019s predictions<\/strong><\/span> on six images :<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-35fa45a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"35fa45a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5375ce4\" data-id=\"5375ce4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-90c652f elementor-widget elementor-widget-image\" data-id=\"90c652f\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-3_BydMeljxkUSkFguGyCEQ.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 8. Example of prediction result, showing the predicted genus vs true genus. (image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d23fd05 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d23fd05\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cc76fe5\" data-id=\"cc76fe5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4a598d4 elementor-widget elementor-widget-heading\" data-id=\"4a598d4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Error Analysis<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1f12ae1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1f12ae1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4785703\" data-id=\"4785703\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-836785b elementor-widget elementor-widget-heading\" data-id=\"836785b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">- Confusion matrix<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-57980e9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"57980e9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-93eb66b\" data-id=\"93eb66b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-55c0972 elementor-widget elementor-widget-text-editor\" data-id=\"55c0972\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The confusion matrix (fig. 9) shows that classes 0, 3, and 8 tend to be classified as classes 7, 4, and 13, respectively :<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bfd3a81 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bfd3a81\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7901621\" data-id=\"7901621\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cd95470 elementor-widget elementor-widget-image\" data-id=\"cd95470\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-dyRBEhcoZ9ST5Po4rYDOOQ.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 9. Confusion matrix from EfficientNet prediction (image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5c6168c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5c6168c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d6fee9b\" data-id=\"d6fee9b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-adb87c4 elementor-widget elementor-widget-text-editor\" data-id=\"adb87c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Literature shows that these confused classes are very similar morphologically and belong to the same taxonomic family (table 3).<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-deeefb3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"deeefb3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4607b87\" data-id=\"4607b87\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1bd52e8 elementor-widget elementor-widget-image\" data-id=\"1bd52e8\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-JAkeplTY0KtrSQ3NTeepZQ.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Table 3. Confused classes.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ca543e9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ca543e9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9d975b4\" data-id=\"9d975b4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5bdc8eb elementor-widget elementor-widget-text-editor\" data-id=\"5bdc8eb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The figure 10 show these morphological similarities between confused classes :<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9e485ce elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9e485ce\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9295d35\" data-id=\"9295d35\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-70e502c elementor-widget elementor-widget-image\" data-id=\"70e502c\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-_v0_lXNVE-Ol8302C8mUxQ.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 10. Morphological similarities between confused classes (source: Wikipwdia).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3abddc2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3abddc2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-71eadbb\" data-id=\"71eadbb\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d974978 elementor-widget elementor-widget-text-editor\" data-id=\"d974978\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"f41e\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">One reason for the confusion of classes may be that the taxonomic identification of mushrooms is wrong. The images we use come from a web page where users classify images of mushrooms only by looking at the images and according to their experience and not according to<span style=\"color: #5e36f3\"><strong> an objective taxonomic identification protocol<\/strong><\/span>.<\/p><p id=\"4374\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">Considering the <strong><span style=\"color: #5e36f3\">confusion matrix<\/span><\/strong> results, we wanted to evaluate our model without one of the confounded genera to see the influence of the data in our model, as show in the table 4.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7afc1f2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7afc1f2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d29b395\" data-id=\"d29b395\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-068bda9 elementor-widget elementor-widget-image\" data-id=\"068bda9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-OvM7bHWEPLUQuMtokEszsg.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Table 4. In red the suppresed genus.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-09d5a10 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"09d5a10\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-006090a\" data-id=\"006090a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c516715 elementor-widget elementor-widget-text-editor\" data-id=\"c516715\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The accuracy values for the 12 genera (without the three eliminated) is 85%, 6% higher than the previous result (79%). This result suggests that we would probably increase our accuracy if we had had a more accurate data set of classifications to train the model.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c9c3885 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c9c3885\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-feddf0a\" data-id=\"feddf0a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c868bbd elementor-widget elementor-widget-heading\" data-id=\"c868bbd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">- Quality of the images<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-efac1d8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"efac1d8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5d681ad\" data-id=\"5d681ad\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f71f680 elementor-widget elementor-widget-text-editor\" data-id=\"f71f680\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We have also noticed that the quality of the images is very diverse (Fig. 11). Indeed, some images contain elements that can prevent the model from identifying the mushroom correctly, such as images with a lot of noise around the mushroom to be classified (images 1 and 2). Images with no mushroom to classify (image 3). Images with more than one mushroom (image 4).<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4a2375d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4a2375d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4252cd4\" data-id=\"4252cd4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2c37507 elementor-widget elementor-widget-image\" data-id=\"2c37507\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-afiVjhVh7UNOs1ZPyF8qYQ.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 11. Exemple of image quality (image by author).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fa9cd3c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fa9cd3c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f023ff1\" data-id=\"f023ff1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-366c606 elementor-widget elementor-widget-heading\" data-id=\"366c606\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Wrapping Up<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b36e778 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b36e778\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a0f5d33\" data-id=\"a0f5d33\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-81ce52f elementor-widget elementor-widget-text-editor\" data-id=\"81ce52f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The summary table below (table 5) shows the accuracy values for the three methods used. We also calculated the <span style=\"color: #5e36f3\"><strong>\u201c<a class=\"au mm\" style=\"color: #5e36f3\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.top_k_accuracy_score.html#sklearn.metrics.top_k_accuracy_score\" target=\"_blank\" rel=\"noopener ugc nofollow\">top-k accuracy classification score<\/a>\u201d<\/strong><\/span> of the package sklearn.metrics (Pedregosa <em class=\"np\">et al.<\/em>, 2011.) with k = 2 and 3 to assess how often the actual category falls between the two or three most likely categories. According to the results, EfficientNet would be the most suitable approach for our mushroom image classification problem with 80% and 86% accuracy.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-069ae2d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"069ae2d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c7a3525\" data-id=\"c7a3525\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6b88dd3 elementor-widget elementor-widget-image\" data-id=\"6b88dd3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/1-mpzE_M7_TTJPg79biOvTlg.jpeg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Table 5. Summary of results.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c171be9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c171be9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7b48b61\" data-id=\"7b48b61\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7090a32 elementor-widget elementor-widget-text-editor\" data-id=\"7090a32\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"61f6\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\"><em class=\"np\">Given the results, computer vision appears as an excellent alternative to the morphological classification of species, if the model could be trained using <\/em><strong class=\"ll ix\"><em class=\"np\"><span style=\"color: #5e36f3\">a large number of good quality<\/span> <span style=\"color: #5e36f3\">images<\/span> <\/em><\/strong><em class=\"np\">and that the species appearing in the image have a <\/em><span style=\"color: #5e36f3\"><strong class=\"ll ix\"><em class=\"np\">taxonomic classification made by a specialist<\/em><\/strong><\/span><em class=\"np\">.<\/em><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2033da5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2033da5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f32a3c2\" data-id=\"f32a3c2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-805fc7b elementor-widget elementor-widget-heading\" data-id=\"805fc7b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Areas of improvement<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2028a5e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2028a5e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-93dc3b9\" data-id=\"93dc3b9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-314031c elementor-widget elementor-widget-text-editor\" data-id=\"314031c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p id=\"fbf4\" class=\"pw-post-body-paragraph lj lk iw ll b lm ln lo lp lq lr ls lt lu lv lw lx ly lz ma mb mc md me mf mg ip fy\" data-selectable-paragraph=\"\">These results can be improved in several ways. The first thing should be to train the model with an adequate <span style=\"color: #5e36f3\"><strong>dataset<\/strong><\/span>, i.e., better quality images and a more detailed taxonomic classification. Also, increasing the number of images in each label (e.g., from 1000 to 2000) could help to reduce morphological classification errors due to subjective human evaluation.<\/p><p id=\"5508\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">Another improvement will be increasing the number of genus. Currently, we trained the model using 15 of the 41 genus that had ~1000 images per label. Furthermore, training the same model to further identify images of mushrooms at the species level can also increase the accuracy.<\/p><p id=\"c6a6\" class=\"pw-post-body-paragraph lj lk iw ll b lm mh lo lp lq mi ls lt lu mj lw lx ly mk ma mb mc ml me mf mg ip fy\" data-selectable-paragraph=\"\">Finally, combining the feature extraction part of our model with a classical classification model, such as <strong><span style=\"color: #5e36f3\">Random Forest<\/span><\/strong> or <strong><span style=\"color: #5e36f3\">SVM<\/span><\/strong>, could improve the classification.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-159e920 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"159e920\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3cf8546\" data-id=\"3cf8546\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0600792 elementor-widget elementor-widget-heading\" data-id=\"0600792\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">References<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d60ede8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d60ede8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a6d862f\" data-id=\"a6d862f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6113322 elementor-widget elementor-widget-text-editor\" data-id=\"6113322\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ol class=\"\"><li id=\"a5dd\" class=\"nw nx iw ll b lm ln lq lr lu oi ly oj mc ok mg or oc od oe fy\" data-selectable-paragraph=\"\">Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. 1998. \u201cGradient-based learning applied to document recognition \u201d.Proceedings of the IEEE.<\/li><li id=\"7d7d\" class=\"nw nx iw ll b lm os lq ot lu ou ly ov mc ow mg or oc od oe fy\" data-selectable-paragraph=\"\">K. Simonyan, A. Zisserman. 2015. \u201cVery Deep Convolutional Networks for Large-Scale Image Recognition\u201d. Proceedings of the ICLR.<\/li><li id=\"6e70\" class=\"nw nx iw ll b lm os lq ot lu ou ly ov mc ow mg or oc od oe fy\" data-selectable-paragraph=\"\">Tan, Mingxing, and Quoc Le. 2019. \u201cEfficientNet: Rethinking Model Scaling for Convolutional Neural Networks\u201d. Proceedings of Machine Learning Research 97.<\/li><li id=\"6d87\" class=\"nw nx iw ll b lm os lq ot lu ou ly ov mc ow mg or oc od oe fy\" data-selectable-paragraph=\"\">F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay. 2011. \u201cScikit-learn: Machine Learning in Python\u201d. Journal of Machine Learning Research.<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Context One of the most widely used and oldest species identification techniques is \u201cmorphological identification\u201d, which identifies individuals by their anatomical characteristics. However, this technique has the disadvantage of not being always accurate since it depends on the observation and the identification protocol of the person who performs it. An alternative to this identification technique [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":165680,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Mushroom recognition : Using Computer Vision in Machine Learning<\/title>\n<meta name=\"description\" content=\"One of the most widely used and oldest species identification techniques is \u201cmorphological identification,\u201d which identifies individuals by their anatomical characteristics.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/mushroom-recognition\" \/>\n<meta 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