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import streamlit as st
import joblib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
st.sidebar.title("Crop & Fertilizer Recommendation System for Sustainable Agriculture")
app_mode = st.sidebar.radio("Select Page",["HOME","CROP RECOMMENDATION","FERTILIZER RECOMMENDATION"])
from PIL import Image
img = Image.open("Diseases.png")
st.image(img)
if(app_mode=="HOME"):
st.markdown("<h1 style='text-align: center;'>Crop & Fertilizer Recommendation System for Sustainable Agriculture", unsafe_allow_html=True)
elif(app_mode=="CROP RECOMMENDATION"):
st.header("Crop Recommendation System")
dtc = joblib.load("crop_prediction_model.pkl")
scaler = joblib.load("crop_scaler.pkl")
crop_dict = {1: 'rice', 2: 'maize', 3: 'chickpea', 4: 'kidneybeans', 5: 'pigeonpeas',
6: 'mothbeans', 7: 'mungbean', 8: 'blackgram', 9: 'lentil', 10: 'pomegranate',
11: 'banana', 12: 'mango', 13: 'grapes', 14: 'watermelon', 15: 'muskmelon',
16: 'apple', 17: 'orange', 18: 'papaya', 19: 'coconut', 20: 'cotton',
21: 'jute', 22: 'coffee'}
N = st.number_input("Nitrogen (N)", min_value=0.0,value=0.0)
P = st.number_input("Phosphorus (P)",min_value=0.0, value=0.0)
K = st.number_input("Potassium (K)", min_value=0.0, value=0.0)
temp = st.number_input("Temperature (°C)", value=0.0)
hum = st.number_input("Humidity (%)",min_value=0.0, max_value=100.0,value=0.0)
ph = st.number_input("pH Level", min_value=0.0, max_value=14.0, value=0.0)
rain = st.number_input("Rainfall (mm)",min_value=0.0, max_value=1200.00, value=0.0)
def crop_rec(N, P, K, temp, hum, ph, rain):
features = np.array([[N, P, K, temp, hum, ph, rain]])
transformed_features = scaler.transform(features)
prediction = dtc.predict(transformed_features)
crop = crop_dict.get(prediction[0], "Unknown Crop")
return f"{crop} is the recommended crop for the given conditions."
if st.button("Predict"):
col1, col2 = st.columns(2)
with col1:
labels = ['Nitrogen (N)', 'Phosphorus (P)', 'Potassium (K)']
sizes = [N, P, K]
colors = ['#ff9999', '#66b3ff', '#99ff99']
def custom_autopct(pct, allsizes):
total = sum(allsizes)
absolute = round(pct / 100. * total, 1)
closest_value = min(allsizes, key=lambda x: abs(x - absolute))
return f' {closest_value} units ({pct:.1f}%)'
fig, ax = plt.subplots(figsize=(6, 6), dpi=100)
ax.pie(sizes, labels=labels, colors=colors,
autopct=lambda pct: custom_autopct(pct, sizes),
startangle=90, wedgeprops=dict(width=0.3))
plt.title('NPK Composition', size=20, color='blue', fontweight='bold')
plt.axis('equal')
st.pyplot(fig)
rfall = (rain / 1200) * 100
sizes = [rfall, 100 - rfall] # Example values
colors = ['#66b3ff', '#D3D3D3']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors,
startangle=90) # Edge color for visibility
plt.title(f'Rain Fall : {rain} mm')
plt.axis('equal')
st.pyplot(fig)
with col2:
sizes = [hum, 100 - hum] # Example values
colors = ['#4169E1', '#D3D3D3']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors,
startangle=90) # Edge color for visibility
plt.title(f'Humidity : {hum} %')
plt.axis('equal')
st.pyplot(fig)
sizes = [temp, 100-temp] # Example values
colors = ['#FFA500', '#D3D3D3']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors,
startangle=90) # Edge color for visibility
plt.title(f'Temperature : {temp}°C')
plt.axis('equal')
st.pyplot(fig)
ph_values = np.linspace(0, 14, 100)
# Define a colormap for pH scale
colors = [
(1, 0, 0), # Red (Strong Acid, pH 0)
(1, 0.5, 0), # Orange
(1, 1, 0), # Yellow
(0, 1, 0), # Green (Neutral, pH 7)
(0, 0, 1), # Blue (Weak Base)
(0.5, 0, 1) # Purple (Strong Base, pH 14)
]
cmap = mcolors.LinearSegmentedColormap.from_list("pH Scale", colors, N=100)
# Create a figure
fig, ax = plt.subplots(figsize=(10, 1))
# Create a gradient color bar
gradient = np.linspace(0, 1, 100).reshape(1, -1)
ax.imshow(gradient, aspect="auto", cmap=cmap, extent=[0, 14, 0, 1])
# Set x-axis labels for pH values
ax.set_xticks(np.arange(0, 15, 1))
ax.set_xticklabels(np.arange(0, 15, 1))
ax.set_yticks([]) # Hide y-axis
# Mark a specific pH point
ph_point = ph # Change this to mark another pH value
ax.scatter(ph_point, 0.5, color="black", s=100, label=f'pH {ph_point}')
ax.vlines(ph_point, 0, 1, color="black", linestyle="dashed", linewidth=1)
# Add title and legend
ax.set_title("pH Scale Visualization", fontsize=12)
ax.legend(loc="upper right")
st.pyplot(fig)
result = crop_rec(N, P, K, temp, hum, ph, rain)
st.success(result)
elif(app_mode=="FERTILIZER RECOMMENDATION"):
st.header("Fertilizer Recommendation System")
# Load the model and scaler
model = joblib.load('fertilizer_prediction_model.pkl')
sc = joblib.load('fertilizer_scaler.pkl')
# Fertilizer dictionary
fert_dict = {1: 'Urea', 2: 'DAP', 3: '14-35-14', 4: '28-28', 5: '17-17-17', 6: '20-20', 7: '10-26-26'}
# Soil type dictionary
soil_type_dict = {0: 'Black', 1: 'Clayey', 2: 'Loamy', 3: 'Red', 4:'Sandy'}
# Crop type dictionary
crop_type_dict = {
0: "Barley",
1: "Cotton",
2: "Ground Nuts",
3: "Maize",
4: "Millets",
5: "Oil seeds",
6: "Paddy",
7: "Pulses",
8: "Sugarcane",
9: "Tobacco",
10: "Wheat"
}
# Function to recommend fertilizer
def recommend_fertilizer(Temparature, Humidity, Moisture, Soil_Type, Crop_Type, Nitrogen, Potassium, Phosphorous):
features = np.array([[Temparature, Humidity, Moisture, Soil_Type, Crop_Type, Nitrogen, Potassium, Phosphorous]])
transformed_features = sc.transform(features)
prediction = model.predict(transformed_features).reshape(1, -1)
fertilizer = [fert_dict[i] for i in prediction[0]]
return f"{fertilizer[0]} is the best fertilizer for the given conditions"
# Streamlit app
Temparature = st.number_input('Temperature', min_value=0.0, max_value=100.0, value=0.0)
Humidity = st.number_input('Humidity', min_value=0.0, max_value=100.0, value=0.0)
Moisture = st.number_input('Moisture', min_value=0.0, max_value=100.0, value=0.0)
Soil_Type = st.selectbox('Soil Type', options=list(soil_type_dict.values()))
Crop_Type = st.selectbox('Crop Type', options=list(crop_type_dict.values()))
Nitrogen = st.number_input('Nitrogen', min_value=0.0, max_value=100.0, value=0.0)
Potassium = st.number_input('Potassium', min_value=0.0, max_value=100.0, value=0.0)
Phosphorous = st.number_input('Phosphorous', min_value=0.0, max_value=100.0, value=0.0)
if st.button('Recommend Fertilizer'):
col1, col2 = st.columns(2)
with col1:
labels = ['Nitrogen (N)', 'Phosphorus (P)', 'Potassium (K)']
sizes = [Nitrogen, Potassium, Phosphorous]
colors = ['#ff9999', '#66b3ff', '#99ff99']
def custom_autopct(pct, allsizes):
total = sum(allsizes)
absolute = round(pct / 100. * total, 1)
closest_value = min(allsizes, key=lambda x: abs(x - absolute))
return f' {closest_value} units ({pct:.1f}%)'
fig, ax = plt.subplots(figsize=(6, 6), dpi=100)
ax.pie(sizes, labels=labels, colors=colors,
autopct=lambda pct: custom_autopct(pct, sizes),
startangle=90, wedgeprops=dict(width=0.3))
plt.title('NPK Composition', size=20, color='blue', fontweight='bold')
plt.axis('equal')
st.pyplot(fig)
sizes = [Humidity, 100 - Humidity] # Example values
colors = ['#4169E1', '#D3D3D3']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors,
startangle=90) # Edge color for visibility
plt.title(f'Humidity : {Humidity} %')
plt.axis('equal')
st.pyplot(fig)
with col2:
sizes = [Moisture, 100 - Moisture] # Example values
colors = ['#4169E1', '#D3D3D3']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors,
startangle=90) # Edge color for visibility
plt.title(f'Moisture : {Moisture} %')
plt.axis('equal')
st.pyplot(fig)
sizes = [Temparature, 100 - Temparature] # Example values
colors = ['#FFA500', '#D3D3D3']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors,
startangle=90) # Edge color for visibility
plt.title(f'Temperature : {Temparature}°C')
plt.axis('equal')
st.pyplot(fig)
# Convert soil and crop types to their numerical values
Soil_Type_num = list(soil_type_dict.keys())[list(soil_type_dict.values()).index(Soil_Type)]
Crop_Type_num = list(crop_type_dict.keys())[list(crop_type_dict.values()).index(Crop_Type)]
result = recommend_fertilizer(Temparature, Humidity, Moisture, Soil_Type_num, Crop_Type_num, Nitrogen,
Potassium, Phosphorous)
st.success(result)
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