{"id":30714730,"url":"https://github.com/shervinnd/wheat_detection","last_synced_at":"2025-09-03T05:15:08.030Z","repository":{"id":310313416,"uuid":"1039424681","full_name":"shervinnd/Wheat_Detection","owner":"shervinnd","description":"Wheat Detection uses YOLO11 to identify wheat heads in images from the GlobalWheat2020 dataset. This project automates dataset download, model training, and inference with bounding box visualization. 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This repository uses the\nYOLO11 model to detect wheat heads in agricultural images, leveraging\nthe GlobalWheat2020 dataset. Perfect for precision agriculture and crop\nmonitoring! 🌱\n\n## 📖 Overview\n\nThis project implements wheat head detection using the Ultralytics YOLO\nframework. It includes downloading the GlobalWheat2020 dataset, training\na YOLO11n model, and performing inference on uploaded images. The\nresults are visualized with bounding boxes around detected wheat heads.\n🌾\n\n## 🚀 Features\n\n-   📥 Downloads and organizes the GlobalWheat2020 dataset\n-   🛠️ Trains a YOLO11n model for wheat head detection\n-   🖼️ Performs inference on uploaded images\n-   📊 Visualizes results with bounding boxes using Matplotlib\n-   ⚙️ Easy-to-use Python script for end-to-end workflow\n\n## 📋 Requirements\n\n-   Python 3.8+\n-   Ultralytics YOLO (`pip install ultralytics`)\n-   Matplotlib\n-   NumPy\n-   Pillow\n-   Google Colab (optional for running in the cloud ☁️)\n\nInstall dependencies:\n\n``` bash\npip install ultralytics matplotlib numpy pillow\n```\n\n## 🛠️ Installation\n\n1.  Clone the repository:\n\n    ``` bash\n    git clone https://github.com/shervinnd/wheat-detection.git\n    cd wheat-detection\n    ```\n\n2.  Install required packages:\n\n    ``` bash\n    pip install -r requirements.txt\n    ```\n\n3.  Run the script in a Python environment or Google Colab:\n\n    ``` bash\n    python wheat_detection.py\n    ```\n\n## 📂 Dataset\n\nThe project uses the **GlobalWheat2020** dataset:\n\n-   Images and annotations are downloaded from Zenodo and Ultralytics.\n-   The dataset is organized into `images`, `labels`, and `annotations`\n    directories.\n-   A YAML file (`GlobalWheat2020_subset.yaml`) is created for training\n    and validation subsets.\n\n## 🧠 Model Training\n\nThe script trains a pre-trained **YOLO11n** model on the GlobalWheat2020\ndataset for 5 epochs. Adjust the number of epochs or image size as\nneeded:\n\n``` python\nmodel.train(data=\"GlobalWheat2020_subset.yaml\", epochs=5, imgsz=640)\n```\n\n## 🔍 Inference\n\nUpload an image to perform wheat head detection:\n\n1.  The script prompts for image upload.\n2.  Results are displayed with bounding boxes using Matplotlib.\n\nExample:\n\n``` python\nresults = model(\"path/to/image.jpg\")\nfor result in results:\n    img = result.plot()\n    plt.imshow(img)\n    plt.show()\n```\n\n## 📊 Results\n\nThe trained model outputs bounding boxes around wheat heads in images.\nResults are saved in the `runs/detect` directory. \n\nContributions are welcome! 🌟 Feel free to:\n\n-   Open issues for bugs or feature requests\n-   Submit pull requests with improvements\n-   Add new datasets or enhance the model\n\n## 📜 License\n\nThis project is licensed under the MIT License. See the LICENSE file for\ndetails.\n\n## 📬 Contact\n\nFor questions or feedback, reach out via GitHub Issues or email at\nshervindanesh8282@gmail.com\n\nHappy wheat detecting! 🌾🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshervinnd%2Fwheat_detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshervinnd%2Fwheat_detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshervinnd%2Fwheat_detection/lists"}