{"id":25841702,"url":"https://github.com/arnab-0053/groot-web-app","last_synced_at":"2026-04-14T05:33:32.312Z","repository":{"id":226901132,"uuid":"769922878","full_name":"ArNAB-0053/groot-web-app","owner":"ArNAB-0053","description":"Groot is a web application that can detect the plant disease just from its leaf.","archived":false,"fork":false,"pushed_at":"2024-07-26T05:57:06.000Z","size":172204,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-01T05:31:36.427Z","etag":null,"topics":["artificial-intelligence","flask","keras","machine-learning","nextjs","tailwindcss","tensorflow","webapp"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ArNAB-0053.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-10T12:52:42.000Z","updated_at":"2024-07-26T05:57:09.000Z","dependencies_parsed_at":"2025-03-01T05:40:27.859Z","dependency_job_id":null,"html_url":"https://github.com/ArNAB-0053/groot-web-app","commit_stats":null,"previous_names":["arnab-0053/groot-web-app"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ArNAB-0053/groot-web-app","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArNAB-0053%2Fgroot-web-app","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArNAB-0053%2Fgroot-web-app/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArNAB-0053%2Fgroot-web-app/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArNAB-0053%2Fgroot-web-app/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ArNAB-0053","download_url":"https://codeload.github.com/ArNAB-0053/groot-web-app/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArNAB-0053%2Fgroot-web-app/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31784253,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-14T02:24:21.117Z","status":"ssl_error","status_checked_at":"2026-04-14T02:24:20.627Z","response_time":153,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","flask","keras","machine-learning","nextjs","tailwindcss","tensorflow","webapp"],"created_at":"2025-03-01T05:30:20.831Z","updated_at":"2026-04-14T05:33:32.297Z","avatar_url":"https://github.com/ArNAB-0053.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Plant Disease Detection System - Groot 🌿\n\nWelcome to the Groot. Groot is a plant leaf disease detection system based on deep learning. It has a training accuracy of 99% and a validation accuracy of 97%, using a model trained with TensorFlow and Keras. The trained model is integrated into a RESTful API with Flask. The system is then developed into a web application using Next.js and Tailwind CSS. \n\n## Overview\n\nThis system consists of:\n- **Model Training**: Utilizing TensorFlow and Keras, I trained a deep learning model with training accuracy of 99% and a validation accuracy of 97%.\n- **API Integration**: Using Flask, I transformed the trained model into a RESTful API for seamless integration.\n- **Web Application**: Built with Next.js and Tailwind CSS, the web app provides an intuitive interface for users to upload images and receive instant disease diagnosis.\n\n## Key Features\n\n- **Accurate Diagnosis**: It diagnoses variety of plant diseases with a high degree of accuracy.\n- **Health Assessment**: It tells whether the plant is healthy or not.\n- **Disease Identification**: Identifies specific diseases that are affecting the plant.\n- **Detailed insights**: In case the plant is not healthy, it provides information on the symptoms and management recommendations.\n\n## Demo\n\nCheck out the [demo video](https://drive.google.com/file/d/1ZRgELyHVwnB7xa-ZIZw3ZW9_dOqiUeP0/view?usp=sharing) to see the system in action!\n\n## Getting Started\n\nTo get started with the Plant Disease Detection System, follow these steps:\n1. Clone the repository.\n2. Install the necessary dependencies.\n3. Run the Flask server to start the API.\n4. Launch the Next.js web app and upload images for diagnosis.\n\n## Technologies Used\n\n- TensorFlow\n- Keras\n- Flask\n- Next.js\n- Tailwind CSS\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnab-0053%2Fgroot-web-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farnab-0053%2Fgroot-web-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnab-0053%2Fgroot-web-app/lists"}