{"id":21135830,"url":"https://github.com/sionpardosi/cornleaf-disease-identification-using-machine-learning","last_synced_at":"2025-04-13T09:38:45.607Z","repository":{"id":259361352,"uuid":"877661273","full_name":"sionpardosi/CornLeaf-Disease-Identification-Using-Machine-Learning","owner":"sionpardosi","description":"Identification of corn leaf diseases using machine learning technology CNN \u0026 Model Transfer Learning DenseNet121 - Identifikasi jenis Penyakit Tanaman pada Daun Jagung Menggunakan Teknologi Machine Learning","archived":false,"fork":false,"pushed_at":"2025-02-17T13:51:42.000Z","size":227071,"stargazers_count":33,"open_issues_count":3,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T01:11:09.864Z","etag":null,"topics":["cnn","cornleafdiseases","desnet","jupyter","machine-learning","transfer-learning"],"latest_commit_sha":null,"homepage":"https://corn-leaf-disease-detector.vercel.app","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/sionpardosi.png","metadata":{"files":{"readme":"README.md","changelog":"history.json","contributing":"CONTRIBUTING.md","funding":".github/FUNDING.yml","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},"funding":{"github":["sionpardosi"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"lfx_crowdfunding":null,"polar":null,"buy_me_a_coffee":null,"thanks_dev":null,"custom":null}},"created_at":"2024-10-24T02:54:33.000Z","updated_at":"2025-03-04T13:02:17.000Z","dependencies_parsed_at":"2025-02-17T14:40:44.508Z","dependency_job_id":null,"html_url":"https://github.com/sionpardosi/CornLeaf-Disease-Identification-Using-Machine-Learning","commit_stats":null,"previous_names":["sionprdsi/cornleaf-disease-identification-using-machine-learning","sionpardosi/cornleaf-disease-identification-using-machine-learning"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sionpardosi%2FCornLeaf-Disease-Identification-Using-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sionpardosi%2FCornLeaf-Disease-Identification-Using-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sionpardosi%2FCornLeaf-Disease-Identification-Using-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sionpardosi%2FCornLeaf-Disease-Identification-Using-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sionpardosi","download_url":"https://codeload.github.com/sionpardosi/CornLeaf-Disease-Identification-Using-Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248692122,"owners_count":21146508,"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":["cnn","cornleafdiseases","desnet","jupyter","machine-learning","transfer-learning"],"created_at":"2024-11-20T06:58:45.576Z","updated_at":"2025-04-13T09:38:40.598Z","avatar_url":"https://github.com/sionpardosi.png","language":"Jupyter Notebook","funding_links":["https://github.com/sponsors/sionpardosi"],"categories":[],"sub_categories":[],"readme":"# Identifikasi Jenis Penyakit Tanaman pada Daun Jagung Menggunakan Teknologi Machine Learning - On Progress\n\n*Identification of corn leaf diseases using machine learning technology* - On Progress.\n\nProyek ini bertujuan untuk mengembangkan sistem deteksi penyakit tanaman jagung melalui **citra daun** menggunakan **Convolutional Neural Network (CNN)** dan **Support Vector Machine (SVM)**. Dengan pendekatan ini, petani dapat mendeteksi penyakit daun jagung secara otomatis dan cepat, yang akan meningkatkan efisiensi serta akurasi dalam diagnosis penyakit daun jagung di lapangan.\n\n### Deskripsi Proyek\nPenelitian ini bertujuan untuk:\n1. Mengembangkan metode deteksi penyakit tanaman jagung melalui citra daun.\n2. Menguji dan membandingkan akurasi model **CNN** dan **SVM**.\n3. Mengimplementasikan model dengan akurasi tertinggi pada sistem deteksi penyakit berbasis citra.\n\nSistem ini dibangun menggunakan microframework **Flask** dengan bahasa pemrograman **Python** untuk memungkinkan integrasi dan pengujian model deteksi penyakit secara fleksibel.\n\n### Pengumpulan Data\nDataset pada penelitian ini diperoleh melalui survei langsung dan observasi di ladang jagung masyarakat, menghasilkan:\n- **500 gambar** dan **10 video** daun jagung.\n- Data tersebut diklasifikasikan ke dalam 4 kelas: **Hawar**, **bercak daun**, **Karat**, dan **Sehat**.\n  \n### **Bukti Observasi Langsung**\n![Bukti Observasi](https://github.com/sionpardosi/CornLeaf-Disease-Identification-Using-Machine-Learning/blob/main/Requirement/observasi%20-%20Copy.jpg)\n\n### Fitur Utama\n\n- **Deteksi Penyakit Daun Jagung**: Mengidentifikasi penyakit seperti _Bercak Daun_ (Leaf Spot), _Karat Jagung_ (Rust), dan _Hawar Daun_ (Blight).\n- **Pemrosesan Citra**: Augmentasi gambar untuk memperkaya dataset dan menghindari overfitting, termasuk proses **rescale**, **rotate**, **zoom**, dan **flip**.\n- **Model Machine Learning**: Algoritma **CNN** dan **SVM** diterapkan untuk klasifikasi gambar.\n- **Evaluasi Model**: Pengukuran akurasi menggunakan **precision**, **recall**, **F1-score**, dan **confusion matrix** untuk hasil yang optimal.\n\n### Teknologi yang Digunakan\n\n- **Confusion matrix** digunakan untuk mengukur akurasi model, dan pengujian dilakukan menggunakan **Jupyter Notebook** / **Google Colab** dan **Visual Studio Code** sebagai teks editor utama.\n- **Python**: Bahasa pemrograman utama untuk pengembangan model.\n- **TensorFlow**: Framework untuk membangun model CNN.\n- **OpenCV**: Digunakan untuk pemrosesan citra dan augmentasi gambar.\n- **Matplotlib**: Untuk visualisasi hasil dan evaluasi model.\n\n---\n\n### Hasil (Coming Soon)\n\n- **Preprocessing**: Tahapan ini mengolah citra untuk mempermudah algoritma CNN dan SVM dalam proses training, dengan menggunakan augmentasi data seperti rescale, rotate, zoom, dan flip.\n- **Pelatihan**: Data latih digunakan untuk mengajari model mengenali ciri-ciri dari setiap jenis penyakit melalui proses iteratif.\n- **Pengujian**: Model yang telah dilatih diuji dengan dataset uji untuk mengukur akurasi dan performanya dalam mendeteksi penyakit.\n- **Evaluasi** -\u003e Coming Soon\n  \n---\n\nDengan adanya sistem ini, diharapkan proses diagnosis penyakit tanaman jagung dapat dilakukan lebih cepat dan efisien.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsionpardosi%2Fcornleaf-disease-identification-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsionpardosi%2Fcornleaf-disease-identification-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsionpardosi%2Fcornleaf-disease-identification-using-machine-learning/lists"}