import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import gradio as gr
import random
# Generate synthetic dataset for Indian crops (focusing on South Indian states)
def generate_synthetic_dataset(num_samples=5000):
np.random.seed(42)
# Common crops in Andhra Pradesh and Telangana
crops = [
'Rice', 'Maize', 'Cotton', 'Groundnut', 'Red Gram (Toor Dal)',
'Green Gram (Moong Dal)', 'Black Gram (Urad Dal)', 'Sunflower',
'Sugarcane', 'Turmeric', 'Chilli', 'Tomato', 'Onion', 'Mango',
'Banana', 'Coconut', 'Soybean', 'Jowar (Sorghum)', 'Bajra (Pearl Millet)'
]
# Soil types common in the region
soil_types = ['Black Cotton', 'Red Sandy', 'Clayey', 'Loamy', 'Sandy Loam']
# Seasons in Indian agriculture
seasons = ['Kharif (June-Oct)', 'Rabi (Oct-Mar)', 'Zaid (Mar-Jun)', 'Whole Year']
# Generate synthetic data
data = {
'Temperature (°C)': np.random.uniform(10, 60, num_samples),
'Rainfall (mm)': np.random.uniform(0, 300, num_samples),
'Humidity (%)': np.random.uniform(20, 100, num_samples),
'Soil pH': np.random.uniform(4.5, 9.5, num_samples),
'Soil Type': np.random.choice(soil_types, num_samples),
'Nitrogen (N) Level': np.random.uniform(0, 150, num_samples),
'Phosphorus (P) Level': np.random.uniform(0, 100, num_samples),
'Potassium (K) Level': np.random.uniform(0, 200, num_samples),
'Season': np.random.choice(seasons, num_samples),
'Crop': np.random.choice(crops, num_samples)
}
# Add some logical patterns based on real-world knowledge
df = pd.DataFrame(data)
# Adjust values based on crop preferences
for idx, row in df.iterrows():
crop = row['Crop']
# Temperature adjustments
if crop in ['Rice', 'Banana', 'Coconut']:
df.at[idx, 'Temperature (°C)'] = np.random.uniform(25, 40)
df.at[idx, 'Humidity (%)'] = np.random.uniform(60, 100)
elif crop in ['Wheat', 'Barley']:
df.at[idx, 'Temperature (°C)'] = np.random.uniform(10, 25)
elif crop in ['Chilli', 'Tomato']:
df.at[idx, 'Temperature (°C)'] = np.random.uniform(20, 35)
# Soil type adjustments
if crop in ['Cotton', 'Groundnut']:
df.at[idx, 'Soil Type'] = 'Black Cotton'
elif crop in ['Rice']:
df.at[idx, 'Soil Type'] = random.choice(['Clayey', 'Loamy'])
# Season adjustments
if crop in ['Rice', 'Maize', 'Cotton', 'Groundnut']:
df.at[idx, 'Season'] = 'Kharif (June-Oct)'
elif crop in ['Wheat', 'Barley', 'Chickpea']:
df.at[idx, 'Season'] = 'Rabi (Oct-Mar)'
elif crop in ['Watermelon', 'Cucumber']:
df.at[idx, 'Season'] = 'Zaid (Mar-Jun)'
# Add profit estimates (in INR per acre)
profit_ranges = {
'Rice': (25000, 50000),
'Maize': (20000, 45000),
'Cotton': (30000, 70000),
'Groundnut': (25000, 55000),
'Red Gram (Toor Dal)': (28000, 60000),
'Green Gram (Moong Dal)': (22000, 50000),
'Black Gram (Urad Dal)': (24000, 52000),
'Sunflower': (18000, 40000),
'Sugarcane': (35000, 75000),
'Turmeric': (40000, 90000),
'Chilli': (50000, 120000),
'Tomato': (30000, 80000),
'Onion': (25000, 65000),
'Mango': (60000, 150000),
'Banana': (50000, 120000),
'Coconut': (40000, 100000),
'Soybean': (22000, 48000),
'Jowar (Sorghum)': (18000, 40000),
'Bajra (Pearl Millet)': (15000, 35000)
}
df['Profit (INR/acre)'] = df['Crop'].apply(lambda x: random.randint(*profit_ranges[x]))
return df
# Generate the dataset
df = generate_synthetic_dataset(10000)
# Prepare data for ML model
X = df.drop(['Crop', 'Profit (INR/acre)'], axis=1)
X = pd.get_dummies(X) # Convert categorical variables to dummy variables
y = df['Crop']
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Crop precautions information
precautions_db = {
'Rice': [
"Maintain proper water level (5-10 cm) in the field",
"Control weeds through manual weeding or herbicides",
"Use balanced fertilizers (N:P:K = 100:50:50 kg/ha)",
"Watch for pests like stem borers and leaf folders"
],
'Maize': [
"Ensure proper spacing (60x20 cm)",
"Apply fertilizers in split doses",
"Control weeds during first 30-40 days",
"Watch for fall armyworm and use pheromone traps"
],
'Cotton': [
"Use drip irrigation for water efficiency",
"Monitor for pink bollworm regularly",
"Practice crop rotation to prevent pest buildup",
"Use recommended spacing (90x60 cm)"
],
'Groundnut': [
"Ensure well-drained soil to prevent fungal diseases",
"Apply gypsum at flowering stage (500 kg/ha)",
"Control weeds during first 45 days",
"Harvest at proper maturity to avoid pod loss"
],
'Red Gram (Toor Dal)': [
"Sow in rows with 45 cm spacing",
"Treat seeds with rhizobium culture",
"Provide protective irrigation during flowering",
"Watch for pod borer and apply neem oil"
],
'Tomato': [
"Use staking for better fruit quality",
"Practice crop rotation to avoid soil diseases",
"Monitor for fruit borer and whitefly",
"Harvest at breaker stage for longer shelf life"
],
'Chilli': [
"Raise seedlings in nursery for 35-40 days",
"Mulch to conserve soil moisture",
"Monitor for thrips and mites regularly",
"Harvest at regular intervals for higher yield"
],
# Default precautions for other crops
'Default': [
"Use recommended spacing for the crop",
"Monitor for pests and diseases regularly",
"Apply balanced fertilizers as per soil test",
"Ensure proper irrigation based on weather conditions"
]
}
# Function to get top precautions based on input features
def get_precautions(crop, temperature, rainfall, humidity, soil_type):
precautions = precautions_db.get(crop, precautions_db['Default'])
# Add weather-specific precautions
if temperature > 35:
precautions.append("Provide mulch to reduce soil temperature")
precautions.append("Increase irrigation frequency during hot days")
if rainfall < 50:
precautions.append("Use water conservation techniques like drip irrigation")
if humidity > 80:
precautions.append("Watch for fungal diseases and apply preventive sprays")
# Add soil-specific precautions
if soil_type == 'Black Cotton':
precautions.append("Practice deep ploughing to break soil hardpans")
elif soil_type == 'Sandy Loam':
precautions.append("Apply organic manure to improve water retention")
return precautions[:5] # Return top 5 precautions
# Function to predict crop and details
def predict_crop(temperature, rainfall, humidity, soil_ph, soil_type, nitrogen, phosphorus, potassium, season):
# Create input dataframe
input_data = {
'Temperature (°C)': [temperature],
'Rainfall (mm)': [rainfall],
'Humidity (%)': [humidity],
'Soil pH': [soil_ph],
'Nitrogen (N) Level': [nitrogen],
'Phosphorus (P) Level': [phosphorus],
'Potassium (K) Level': [potassium],
'Season': [season]
}
# Add soil type columns (one-hot encoding)
for st in ['Black Cotton', 'Red Sandy', 'Clayey', 'Loamy', 'Sandy Loam']:
input_data[f'Soil Type_{st}'] = [1 if soil_type == st else 0]
# Add season columns (one-hot encoding)
for s in ['Kharif (June-Oct)', 'Rabi (Oct-Mar)', 'Zaid (Mar-Jun)', 'Whole Year']:
input_data[f'Season_{s}'] = [1 if season == s else 0]
input_df = pd.DataFrame(input_data)
# Ensure columns are in same order as training data
input_df = input_df.reindex(columns=X.columns, fill_value=0)
# Predict crop
crop = model.predict(input_df)[0]
# Get profit range
profit = df[df['Crop'] == crop]['Profit (INR/acre)'].mean()
# Get precautions
precautions = get_precautions(crop, temperature, rainfall, humidity, soil_type)
# Get similar crops (top 3 alternatives)
probas = model.predict_proba(input_df)[0]
top3_idx = np.argsort(probas)[-3:][::-1]
similar_crops = [model.classes_[i] for i in top3_idx if model.classes_[i] != crop][:2]
# Prepare output
output = {
"Recommended Crop": crop,
"Expected Profit (INR per acre)": f"₹{int(profit):,}",
"Top Precautions": precautions,
"Alternative Crops": similar_crops,
"Best Season": season
}
return output
# Custom CSS for farmer-friendly interface
custom_css = """
/* Main container styling */
.agrismart-container {
background: linear-gradient(135deg, #f5f7fa 0%, #e4efe9 100%);
border-radius: 15px;
padding: 20px;
box-shadow: 0 10px 20px rgba(0,0,0,0.1);
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
/* Header styling */
.agrismart-header {
background: linear-gradient(to right, #4CAF50, #2E8B57);
color: white;
padding: 15px 20px;
border-radius: 10px;
text-align: center;
margin-bottom: 20px;
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
}
/* Input section styling */
.agrismart-input {
background-color: rgba(255, 255, 255, 0.9);
padding: 20px;
border-radius: 10px;
margin-bottom: 20px;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
}
/* Output section styling */
.agrismart-output {
background-color: #ffffff;
padding: 20px;
border-radius: 10px;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
border-left: 5px solid #4CAF50;
}
/* Button styling */
.agrismart-button {
background: linear-gradient(to right, #4CAF50, #2E8B57) !important;
color: white !important;
border: none !important;
padding: 12px 25px !important;
border-radius: 8px !important;
font-size: 16px !important;
cursor: pointer !important;
transition: all 0.3s !important;
box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important;
}
.agrismart-button:hover {
transform: translateY(-2px) !important;
box-shadow: 0 6px 8px rgba(0,0,0,0.15) !important;
}
/* Slider styling */
.agrismart-slider .gr-slider {
background: #e0e0e0 !important;
height: 10px !important;
border-radius: 5px !important;
}
.agrismart-slider .gr-slider .gr-slider-selection {
background: linear-gradient(to right, #4CAF50, #2E8B57) !important;
}
/* Label styling */
.agrismart-label {
font-weight: bold !important;
color: #2E8B57 !important;
margin-bottom: 5px !important;
font-size: 16px !important;
}
/* Dropdown styling */
.agrismart-dropdown {
border: 1px solid #ddd !important;
border-radius: 8px !important;
padding: 8px 12px !important;
box-shadow: inset 0 1px 3px rgba(0,0,0,0.1) !important;
}
/* Result card styling */
.agrismart-result-card {
background: #f9f9f9;
border-radius: 10px;
padding: 15px;
margin: 10px 0;
border-left: 4px solid #4CAF50;
}
.agrismart-result-title {
color: #2E8B57;
font-weight: bold;
margin-bottom: 10px;
}
.agrismart-result-value {
font-size: 18px;
color: #333;
}
/* Precautions list styling */
.agrismart-precautions {
list-style-type: none;
padding-left: 0;
}
.agrismart-precautions li {
background: url('data:image/svg+xml;utf8,') no-repeat left center;
padding-left: 25px;
margin-bottom: 8px;
line-height: 1.5;
}
/* Responsive design */
@media (max-width: 768px) {
.agrismart-container {
padding: 10px;
}
}
"""
# Function to format outputs
def format_outputs(output):
crop_md = f"**Recommended Crop:** {output['Recommended Crop']}"
profit_md = f"**Expected Profit (INR per acre):** {output['Expected Profit (INR per acre)']}"
season_md = f"**Best Season:** {output['Best Season']}"
alt_md = f"**Alternative Crops:** {', '.join(output['Alternative Crops'])}"
prec_html = """
""" + "\n".join([f"
{p}
" for p in output['Top Precautions']]) + """
"""
return crop_md, profit_md, prec_html, alt_md, season_md
# Create Gradio interface
with gr.Blocks(css=custom_css) as demo:
with gr.Column(elem_classes="agrismart-container"):
with gr.Row(elem_classes="agrismart-header"):
gr.Markdown("""
# 🌱 Crop Vision : Smart Crop Advisor for Farmers
### Get personalized crop recommendations based on your farm conditions
""")
with gr.Row():
with gr.Column(elem_classes="agrismart-input"):
gr.Markdown("### 🌦️ Enter Your Farm Conditions", elem_classes="agrismart-label")
with gr.Row():
temperature = gr.Slider(10, 60, label="1. Temperature (How hot is your area?)",
info="Measure the air temperature in shade (°C)",
elem_classes="agrismart-slider")
rainfall = gr.Slider(0, 300, label="2. Rainfall (How much rain your area gets?)",
info="Annual rainfall in your area (mm)",
elem_classes="agrismart-slider")
with gr.Row():
humidity = gr.Slider(20, 100, label="3. Humidity (How moist is your air?)",
info="Relative humidity percentage (%)",
elem_classes="agrismart-slider")
soil_ph = gr.Slider(4, 10, label="4. Soil pH (Is your soil acidic or alkaline?)",
info="7 is neutral, below 7 is acidic, above 7 is alkaline",
elem_classes="agrismart-slider")
with gr.Row():
soil_type = gr.Dropdown(
["Black Cotton", "Red Sandy", "Clayey", "Loamy", "Sandy Loam"],
label="5. Soil Type (What type of soil do you have?)",
info="Black Cotton is common in Andhra/Telangana",
elem_classes="agrismart-dropdown"
)
season = gr.Dropdown(
["Kharif (June-Oct)", "Rabi (Oct-Mar)", "Zaid (Mar-Jun)", "Whole Year"],
label="6. Season (When will you cultivate?)",
elem_classes="agrismart-dropdown"
)
with gr.Row():
nitrogen = gr.Slider(0, 150, label="7. Nitrogen Level (N) in soil",
info="Essential for leaf growth (kg/ha)",
elem_classes="agrismart-slider")
phosphorus = gr.Slider(0, 100, label="8. Phosphorus Level (P) in soil",
info="Important for root development (kg/ha)",
elem_classes="agrismart-slider")
potassium = gr.Slider(0, 200, label="9. Potassium Level (K) in soil",
info="Helps in fruit quality (kg/ha)",
elem_classes="agrismart-slider")
submit_btn = gr.Button("Get Crop Recommendation", elem_classes="agrismart-button")
with gr.Column(elem_classes="agrismart-output"):
gr.Markdown("### 📊 Recommended Crop Details", elem_classes="agrismart-label")
with gr.Column(elem_classes="agrismart-result-card"):
crop = gr.Markdown("**Recommended Crop:** ", elem_classes="agrismart-result-value")
profit = gr.Markdown("**Expected Profit (INR per acre):** ", elem_classes="agrismart-result-value")
season_out = gr.Markdown("**Best Season:** ", elem_classes="agrismart-result-value")
alternatives = gr.Markdown("**Alternative Crops:** ", elem_classes="agrismart-result-value")
gr.Markdown("### 🛡️ Top Precautions", elem_classes="agrismart-result-title")
precautions = gr.HTML("""
Enter your farm details and click the button to get recommendations
""")
# Example images (would need actual images in production)
gr.Markdown("### 🌾 Common Crops in Andhra/Telangana")
gr.HTML("""