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https://github.com/opencodeiiita/pestering-data
Build an image classification model working with a real world dataset.
https://github.com/opencodeiiita/pestering-data
deep-learning image-classification keras-tensorflow neural-networks opencode24 python transfer-learning transformers
Last synced: 4 days ago
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Build an image classification model working with a real world dataset.
- Host: GitHub
- URL: https://github.com/opencodeiiita/pestering-data
- Owner: opencodeiiita
- Created: 2024-12-11T13:06:05.000Z (29 days ago)
- Default Branch: main
- Last Pushed: 2024-12-23T13:40:25.000Z (17 days ago)
- Last Synced: 2024-12-23T14:33:03.818Z (17 days ago)
- Topics: deep-learning, image-classification, keras-tensorflow, neural-networks, opencode24, python, transfer-learning, transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 82.4 MB
- Stars: 1
- Watchers: 3
- Forks: 34
- Open Issues: 3
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🌿 **Pestering-Data**
**Using Deep Learning to Classify Crop Health and Diseases**
---
## 🚀 **Overview**
The use of **Artificial Intelligence (AI)** in agriculture has become increasingly prominent in recent years. AI in agriculture primarily aims to **enhance crop productivity**, **manage pests and diseases**, and **reduce operational costs**. In many developing countries, the agricultural sector faces significant challenges, including **crop diseases and pest infestations**, **limited access to technological knowledge** among farmers, and **inadequate storage infrastructure**, among other issues.
This project introduces a **large-scale dataset** comprising **22 classes** divided into **4 main crops**:
- 🌰 **Cashew**
- 🥔 **Cassava**
- 🌽 **Maize**
- 🍅 **Tomato**
Each category has further subdivisions:
- **Healthy classes**
- **Disease-specific classes**
### 🎯 **Goal**
Leverage modern **Deep Learning** techniques to create a model that accurately classifies crop images as either **diseased** or **healthy**.
📂 **Dataset Structure:**
Each crop folder contains:
- **train_set/** for training
- **test_set/** for testing
🔗 **Dataset Link:** [**Kaggle Dataset**](https://kaggle.com/datasets/70386cefea61cfef7efab20c1a430a79a734ef495661efc02b2630b98d8cafc7)
---
## 📜 **Instructions**
- **Do not modify any pre-written code or comments.**
- Write your code **only in the provided space**.
- Add **meaningful comments** to ensure smooth code reviews.
- Create a **Pull Request (PR)** as per the issue guidelines.
- Join the **Discord server** for any queries or clarifications.
- **Strictly refrain from any kind of plagiarism** to avoid any sort of disqualification.
---
## 🔄 **Procedure**
1. **Download** the dataset from the link provided above.
2. **Fork** this repository and **clone** it to your local machine. *(You may need to re-clone after each task.)*
3. **Naming Conventions:**
- **IIIT Allahabad Students:** Name files as **IIT2023098**, where:
- **IIT** = Your branch
- **2023098** = Your unique ID
- **Other College Participants:** Name files as **COLLEGE_ROLLNO** (e.g., **IITBHU_123456**).
4. **File Placement:**
- Push your **.ipynb solution files** to the **correct folder**:
- **Example:** Place the solution for **Task1** in **Task1_solutions/**.
5. **Submit a Pull Request:**
- Your **PR** will be reviewed by mentors.
- Only **relevant PRs** will be merged and **awarded points**.
---
## 💡 **Guidelines for Pull Requests**
- Follow the **naming conventions** strictly.
- Use **comments** to explain your approach.
- Push solutions to the **correct folder** only.
---
## 💬 **Need Help?**
- **Contact Email:** [**[email protected]**](mailto:[email protected])
- **Join the Discussion:** [**Discord Server**](https://discord.gg/bnGquU7C)
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### 🔧 **Contribute, Collaborate, and Innovate!** 🚀
Let’s make farming smarter with the power of **AI and Deep Learning**!
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