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# Crop Recommendation
#### Harnessing the capabilities of machine learning models, analyzes specific parameters to suggest the most suitable crops, optimizing yields and efficiency.
## Demo
### Input Interface
<img src="https://github.com/07Sada/crop-recommendation/assets/112761379/3f8c5f4d-1df4-4516-b428-f4b95a2cc5df" alt="Image 1" width="800">
### Output Interface
<img src="https://github.com/07Sada/crop-recommendation/assets/112761379/86a4aefd-b973-40ad-b79c-f2b1dd070d91" alt="Image 1" width="800">
## Data Source
This dataset contains information about the soil and environmental conditions that are ideal for growing different crops. The dataset includes the following columns:
- `N`: The ratio of nitrogen content in the soil.
- `P`: The ratio of phosphorus content in the soil.
- `K`: The ratio of potassium content in the soil.
- `Temperature`: The temperature in degrees Celsius.
- `Humidity`: The relative humidity in percent.
- `pH`: The pH value of the soil.
- `Rainfall`: The rainfall in millimeters.
[Link](https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset) for the dataset
<details>
<summary>Supported crops
</summary>
- Apple
- Blueberry
- Cherry
- Corn
- Grape
- Pepper
- Orange
- Peach
- Potato
- Soybean
- Strawberry
- Tomato
- Squash
- Raspberry
</details>
## Project Details
This is repository is submodule for [CropGaurd](https://github.com/07Sada/CropGaurd.git)
## Project PipeLine Stages
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