import streamlit as st import pandas as pd import numpy as np import joblib # Load the trained model model_path = 'random_forest_regressor_model.pkl' model = joblib.load(model_path) # Encoding mappings rainfall_mapping = {'moderate': 2, 'low': 1, 'high': 0} soil_type_mapping = {'sandy': 3, 'silty': 4, 'clay': 0, 'peaty': 2, 'loamy': 1} drainage_mapping = {'poor': 2, 'moderate': 1, 'good': 0} crop_mapping = {'rice': 2, 'wheat': 4, 'soybean': 3, 'cotton': 0, 'maize': 1} growth_stage_mapping = {'flowering': 0, 'fruiting': 1, 'vegetative': 5, 'seedling': 4, 'reproductive': 3, 'maturity': 2} # Function to preprocess user inputs def preprocess_input(data): data['rainfall_pattern'] = rainfall_mapping[data['rainfall_pattern']] data['soil_type'] = soil_type_mapping[data['soil_type']] data['drainage_properties'] = drainage_mapping[data['drainage_properties']] data['crop_type'] = crop_mapping[data['crop_type']] data['growth_stage'] = growth_stage_mapping[data['growth_stage']] return np.array(list(data.values())).reshape(1, -1) # Streamlit App st.title("💧Optimal Water Requirement Prediction for Agricultural Farms 🌱") st.write("This application predicts the optimal water requirement for different crops based on environmental and soil properties.") # User Inputs st.sidebar.header("Input Features") # Numerical inputs temperature = st.sidebar.slider("Temperature (°C):", min_value=-10.0, max_value=50.0, value=25.0, step=0.1) humidity = st.sidebar.slider("Humidity (%):", min_value=0.0, max_value=100.0, value=50.0, step=0.1) wind_speed = st.sidebar.slider("Wind Speed (m/s):", min_value=0.0, max_value=20.0, value=5.0, step=0.1) evapotranspiration = st.sidebar.slider("Evapotranspiration (mm/day):", min_value=0.0, max_value=10.0, value=3.0, step=0.1) soil_moisture_levels = st.sidebar.slider("Soil Moisture Levels (%):", min_value=0.0, max_value=100.0, value=50.0, step=0.1) water_retention_capacity = st.sidebar.slider("Water Retention Capacity (%):", min_value=0.0, max_value=100.0, value=60.0, step=0.1) crop_water_requirement = st.sidebar.slider("Actual Water Requirement (mm/day)):", min_value=0.0, max_value=25.0, value=10.0, step=0.1) # Categorical inputs rainfall_pattern = st.sidebar.selectbox("Rainfall Pattern:", list(rainfall_mapping.keys())) soil_type = st.sidebar.selectbox("Soil Type:", list(soil_type_mapping.keys())) drainage_properties = st.sidebar.selectbox("Drainage Properties:", list(drainage_mapping.keys())) crop_type = st.sidebar.selectbox("Crop Type:", list(crop_mapping.keys())) growth_stage = st.sidebar.selectbox("Growth Stage:", list(growth_stage_mapping.keys())) # Prediction if st.sidebar.button("💧 Predict Water Requirement"): input_data = { 'temperature': temperature, 'humidity': humidity, 'wind_speed': wind_speed, 'evapotranspiration': evapotranspiration, 'soil_moisture_levels': soil_moisture_levels, 'water_retention_capacity': water_retention_capacity, 'rainfall_pattern': rainfall_pattern, 'soil_type': soil_type, 'drainage_properties': drainage_properties, 'crop_type': crop_type, 'growth_stage': growth_stage, 'crop_water_requirement':crop_water_requirement } processed_data = preprocess_input(input_data) prediction = model.predict(processed_data)[0] st.write("### Predicted Water Requirement") st.success(f"The predicted water requirement is **{prediction:.2f} mm/day**.") st.header("Optimization Suggestions") suggestions = [] # Check soil type if soil_type == 3: suggestions.append("Consider improving soil structure by adding organic matter or compost to enhance water retention.") # Check evapotranspiration if evapotranspiration > 8.0: suggestions.append("High evapotranspiration detected. Use mulching to reduce evaporation and conserve soil moisture.") # Check soil moisture if soil_moisture_levels < 15.0: suggestions.append("Low soil moisture detected. Ensure proper irrigation scheduling to maintain adequate levels.") # Check water retention if water_retention_capacity < 20.0: suggestions.append("Low water retention capacity. Incorporate soil amendments to improve retention.") # Check drainage if drainage_properties=='0': suggestions.append("Poor drainage detected. Improve soil aeration or use soil conditioners to balance drainage.") # Display suggestions if suggestions: for suggestion in suggestions: st.write(f"- {suggestion}") else: st.write("✅ All parameters are well-balanced for optimal water usage!") # Footer st.write("---") import datetime # Get current year dynamically current_year = datetime.datetime.now().year st.markdown(f"""

© {current_year} Aditya Manwatkar | GitHub | LinkedIn

""", unsafe_allow_html=True)