Data Science for Agriculture
Rainfall, Soil, Temperature-Based Predictions
1. Introduction
The Crop Yield Predictor project aims to utilize data science techniques to
predict crop yields based on environmental and soil-related factors such as
rainfall, temperature, and soil properties.
This tool will aid farmers and agricultural planners in making informed
decisions to maximize productivity.
2. Project Workflow
1. Problem Definition:
- Accurately predict crop yields using
historical and environmental data.
- Support sustainable agriculture by
optimizing resource use based on predictions.
2. Data Collection:
- Sources: Agricultural datasets,
meteorological data, and soil condition databases.
- Features: Rainfall, soil type, pH
level, temperature, humidity, and crop type.
3. Data Preprocessing:
- Handle missing values, normalize
continuous variables, and encode categorical variables.
4. Model Building:
- Regression models like Random
Forest, Gradient Boosting, or Neural Networks.
5. Evaluation:
- Metrics: Mean Absolute Error (MAE),
Mean Squared Error (MSE), R-squared.
6. Deployment:
- Build a dashboard for real-time
predictions using Flask, Streamlit, or Dash.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn,
Plotly
- Machine Learning: Scikit-learn,
TensorFlow/Keras, XGBoost
- Dashboard Development: Streamlit or
Dash
4. Implementation Steps
Step 1: Setup Environment
Install the required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn xgboost streamlit
```
Step 2: Data Preprocessing
Load and clean the dataset:
```
import pandas as pd
data = pd.read_csv("agriculture_data.csv")
print(data.head())
# Handle missing values
data.fillna(method='ffill', inplace=True)
# Encode categorical variables
data = pd.get_dummies(data, columns=['soil_type', 'crop_type'],
drop_first=True)
```
Normalize numerical features:
```
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data[['rainfall', 'temperature', 'humidity']] =
scaler.fit_transform(data[['rainfall', 'temperature', 'humidity']])
```
Step 3: Build and Train the Model
Split the dataset:
```
from sklearn.model_selection import train_test_split
X = data.drop(columns=['yield'])
y = data['yield']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
```
Train a Random Forest Regressor:
```
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
```
Evaluate the model:
```
from sklearn.metrics import mean_absolute_error, r2_score
y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"Mean Absolute Error: {mae}")
print(f"R-squared: {r2}")
```
Step 4: Build the Dashboard
Develop a Streamlit dashboard for predictions:
```
import streamlit as st
st.title("Crop Yield Predictor")
rainfall = st.number_input("Rainfall (mm):")
temperature = st.number_input("Temperature (°C):")
humidity = st.number_input("Humidity (%):")
if st.button("Predict Yield"):
input_data =
scaler.transform([[rainfall, temperature, humidity]])
prediction =
model.predict(input_data)
st.write(f"Predicted Yield:
{prediction[0]:.2f} tons/hectare")
```
5. Expected Outcomes
1. A robust model capable of predicting crop yields based on environmental and
soil features.
2. An interactive dashboard enabling real-time predictions for various input
scenarios.
3. Improved decision-making in agricultural planning and resource allocation.
6. Additional Suggestions
- Incorporate advanced models like Gradient Boosting Machines or LSTMs for
better accuracy.
- Add GIS-based visualization for geospatial insights on crop yield.
- Include real-time weather data from APIs for dynamic predictions.
- Integrate feedback mechanisms for continuous model improvement.