Krishi Kiran provides a complete interface for the farmers as well as consumers for buying and selling crops in the web app. In this web app we are integrating AI CHATBOT for the farmers through which the farmers can ask any desired questions they want. Also through our ML model the farmers can check whether the crop has been been affected by disease or not. For the consumers we have added 'Compare crops' feature so that they can compare the prices of the crops uploaded by farmers. This makes Krishi Kiran a complete web-app which acts as a e-commerce platform with seller as farmers themselves and most importantly it acts a complete tool and all in 1 web-app for farmers.
1)Upload Image: Go to the Disease Recognition page and upload an image of a plant with suspected diseases.
2)Analysis: Our system will process the image using advanced algorithms to identify potential diseases.
3)Results: View the results and recommendations for further action.
Make sure to add the path of MySQL
Open a new folder in VS code and in the terminal enter the following :
Initializing the git
git int
Cloning the git-repo
git clone https://github.com/VasuOOCh/byteverse_TeamAgora
installing node modules
npm install
const connection = mysql.createConnection({
host: 'hostname',
user: 'user',
database: 'agora_byte',
password : process.env.MYSQL_PASS //Enter MySQL password
});
const anthropic = new Anthropic({
apiKey: process.env.API_KEY // Enter your API key here
});
async function answer(ques) {
const message = await anthropic.messages.create({
max_tokens: 1024,
messages: [{ role: 'user', content: ques }],
model: 'claude-3-opus-20240229',
});
return message.content[0].text;
}create database agora_byte;
use agora_byte;
create table farmer_info (
farm_id varchar(100) PRIMARY KEY,
farm_name varchar(100),
mobile bigint,
amount int default 0,
complete int default 0,
pending int default 0
);
- "@anthropic-ai/sdk": "^0.20.1",
- "axios": "^1.6.8",
- "dotenv": "^16.4.5",
- "ejs": "^3.1.9",
- "express": "^4.19.2", 6)"method-override": "^3.0.0",
- "mysql2": "^3.9.4",
- "uuidv4": "^6.2.13"
Please make sure to update tests as appropriate.
we have used Python 3.12.3.
- matplotlib==3.8.4
- numpy==1.26.4
- pandas==2.2.1
- scikit-learn==1.4.2
- seaborn==0.13.2
- streamlit==1.33.0
- tensorflow==2.16.1
