KRISHI AI 🌿

Our digital agriculture project aims to empower farmers by leveraging the power of AI to make informed decisions about their crops. Through our platform, farmers can input data about their soil and crop, and receive personalized recommendations on the right fertilizer to use and the best crop to grow based on their unique conditions.

In addition, our AI-powered disease prediction system analyzes data from various sources such as weather, soil, and crop health to help farmers detect and predict diseases early on. This allows them to take preventative measures and minimize the impact of the disease on their crop yield.

Our platform is user-friendly, accessible, and affordable, making it easy for farmers of all sizes and backgrounds to benefit from the latest technology in agriculture. We believe that by empowering farmers with accurate information and recommendations, we can help them to increase their yields, optimize their resources, and ultimately contribute to a more sustainable and prosperous future for agriculture.

MOTIVATION 💪

Farming is one of the major sectors that influences a country’s economic growth.
In country like India, majority of the population is dependent on agriculture for their livelihood. Many new technologies, such as Machine Learning and Deep Learning, are being implemented into agriculture so that it is easier for farmers to grow and maximize their yield.
In this project, we present a website in which the following applications are implemented; Crop recommendation, Fertilizer recommendation and Plant disease prediction, respectively.
- In the crop recommendation application, the user can provide the soil data from their side and the application will predict which crop should the user grow.
- For the fertilizer recommendation application, the user can input the soil data and the type of crop they are growing, and the application will predict what the soil lacks or has excess of and will recommend improvements.
- For the last application, that is the plant disease prediction application, the user can input an image of a diseased plant leaf, and the application will predict what disease it is and will also give a little background about the disease and suggestions to cure it.
Our project features a website that incorporates three distinct applications: Crop Recommendation, Fertilizer Recommendation, and Plant Disease Prediction.
- The Crop Recommendation tool enables users to input their soil data, and based on that information, the application recommends which crop is most suitable for their specific conditions.
- With the Fertilizer Recommendation tool, users can input both their soil data and the type of crop they intend to grow. Based on this information, the application assesses which nutrients may be lacking or in excess, and provides recommendations to optimize soil conditions.
- The Plant Disease Prediction application allows users to upload an image of a diseased plant leaf. The application utilizes AI to predict the type of disease present, and provides users with relevant background information and suggested remedies to address the issue

DATA SOURCE 📊

Crop recommendation dataset (custom built dataset)
Fertilizer suggestion dataset (custom built dataset)
Disease detection dataset

Built with 🛠️

How to use 💻

Crop Recommendation system ==> enter the corresponding nutrient values of your soil, state and city. Note that, the N-P-K (Nitrogen-Phosphorous-Pottasium) values to be entered should be the ratio between them. Refer this website for more information. Note: When you enter the city name, make sure to enter mostly common city names. Remote cities/towns may not be available in the Weather API from where humidity, temperature data is fetched.
Fertilizer suggestion system ==> Enter the nutrient contents of your soil and the crop you want to grow. The algorithm will tell which nutrient the soil has excess of or lacks. Accordingly, it will give suggestions for buying fertilizers.
Disease Detection System ==> Upload an image of leaf of your plant. The algorithm will tell the crop type and whether it is diseased or healthy. If it is diseased, it will tell you the cause of the disease and suggest you how to prevent/cure the disease accordingly. Note that, for now it only supports following crops

Supported crops

Apple
Blueberry
Cherry
Corn
Grape
Pepper
Orange
Peach
Potato
Soybean
Strawberry
Tomato
Squash
Raspberry

How to run locally 🛠️

Before the following steps make sure you have git, Anaconda or miniconda installed on your system
Clone the complete project with git clone https://github.com/notahuman-1-0/KrishiAI.git or you can just download the code and unzip it
Note: The master branch doesn't have the updated code used for deployment, to download the updated code used for deployment you can use the following command
```
❯ git clone -b deploy https://github.com/notahuman-1-0/KrishiAI.git
```
deploy branch has only the code required for deploying the app (rest of the code that was used for training the models, data preparation can be accessed on master branch)
It is highly recommended to clone the deploy branch for running the project locally (the further steps apply only if you have the deploy branch cloned) run the project with
```
python app.py
```
Open the localhost url provided after running app.py and now you can use the project locally in your web browser.

DEMO

Crop recommendation system

Fertilizer suggestion system

Disease Detection system

ch33nchan/KrishiAI