{"id":29196560,"url":"https://github.com/brianrscode/cultivai","last_synced_at":"2025-07-02T06:07:20.922Z","repository":{"id":301847719,"uuid":"1007553577","full_name":"brianrscode/CultivAI","owner":"brianrscode","description":"Aplicación web de machine learning para recomendación de cultivos agrícolas","archived":false,"fork":false,"pushed_at":"2025-06-29T06:19:51.000Z","size":715,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-29T07:27:16.143Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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/brianrscode.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,"zenodo":null}},"created_at":"2025-06-24T07:13:25.000Z","updated_at":"2025-06-29T06:19:54.000Z","dependencies_parsed_at":"2025-06-29T07:37:28.543Z","dependency_job_id":null,"html_url":"https://github.com/brianrscode/CultivAI","commit_stats":null,"previous_names":["brianrscode/cultivai"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/brianrscode/CultivAI","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/brianrscode%2FCultivAI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/brianrscode%2FCultivAI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/brianrscode%2FCultivAI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/brianrscode%2FCultivAI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/brianrscode","download_url":"https://codeload.github.com/brianrscode/CultivAI/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/brianrscode%2FCultivAI/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263083717,"owners_count":23411165,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":[],"created_at":"2025-07-02T06:07:20.175Z","updated_at":"2025-07-02T06:07:20.900Z","avatar_url":"https://github.com/brianrscode.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🌱 CultivAI\n\u003cp\u003eAplicación web de machine learning que sirve para la recomendación de cultivos agrícolas\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n    \u003cimg src=\"./imgs/cultivai.jpeg\" width=\"300px\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n## 📌 Características\n- Modelo de clasificación entrenado con Random Forest utilizando un conjunto de datos agrícola.\n- Predicción basada en parámetros como niveles de nitrógeno (N), fósforo (P), potasio (K), temperatura, humedad, pH y precipitación.\n\n## 🚀 Instalación\n\n1. Clona el repositorio:\n```bash\ngit clone https://github.com/brianrscode/cultivai.git\ncd cultivai\n```\n2. Crea un entorno virtual\n```bash\npython -m venv venv\n```\n\n3. Activa el entorno virtual\n    - En Windows:\n\n    ```bash\n    venv\\Scripts\\activate\n    ```\n\n    - En macOS y Linux:\n\n    ```bash\n    source venv/bin/activate\n    ```\n\n4. Instala las dependencias:\n```bash\npip install -r requirements.txt\n```\n\n5. Ejecuta el servidor de desarrollo:\n```bash\npython manage.py runserver\n```\n\n## 🧪 Cómo usar CultivAI\n- Ingresa los valores de N, P, K, temperatura, humedad, pH, y precipitación.\n- El modelo procesará los datos y devolverá el cultivo más adecuado.\n- Visualiza el resultado directamente en la página web.\n\n## 📊 Dataset\n\nFuente: [Kaggle - Crop Recommendation Dataset](https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset/data)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrianrscode%2Fcultivai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrianrscode%2Fcultivai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrianrscode%2Fcultivai/lists"}