Agricultural-crop-Prediction
:wave: Hi in this data science project I am going to predict the ‘Crop’ based on soil proerties and climate.
In below video you can check the workflow and overview of this project.:point_down:
https://user-images.githubusercontent.com/126685886/230735981-8be7621f-7053-4534-ab8b-64f0d7a8b6f5.mp4
To access this webapp use below link:point_down:
https://engineer-aman-agricultural-crop-prediction-agri-model-ejr8j6.streamlit.app/
A few weeks ago, I saw farmers working hardly and devoutly to grow crops for us. I was happy to see them work so hard. But I found out that even after so much struggle our farmers grow crops to some extent. They failed to maximize crop growth. It made me think about why farmers are not able to increase their productivity. Then I started thinking about the reasons why our agronomist could not reach maximum productivity.
Many factors affect the productivity of crops but two factors that affect crops productivity directly are,
1.Soil Conditions 2.Climate Conditions
Keeping these factors in my mind, I tried to build a Machine Learning model that will take different Climate and Soil conditions and predict a crop that can make maximum growth in these certain conditions and I did. By using this model, agronomists will be able to know which crop is more productive.
Let me tell you what I did and what I learned in this project…
Tools Used:-
1.Jupyter notebook 2.Streamlit 3.Python
About Data Set
I got the data Set from Kaggle to build such a model.
df.head()
Nitrogen Phosphorus Potassium temperature humidity ph rainfall label
90 42 43 20.879744 82.002744 6.502985 202.935536 rice
85 58 41 21.770462 80.319644 7.038096 226.655537 rice
60 55 44 23.004459 82.320763 7.840207 263.964248 rice
74 35 40 26.491096 80.158363 6.980401 242.864034 rice
78 42 42 20.130175 81.604873 7.628473 262.717340 rice
df.shape
(2200, 8)
Columns and there Descrption are as below :point_down:
N: Ratio of Nitrogen in the Soil
P: Ratio of Phosphorous in the Soil
K: Ratio of Potassium in the Soil
Temperature: Temperature in Celsius
Humidity: Relative Humidity in %
PH: Ph value for the Soil
Rain Fall: Rain Fall in 1mm
Data Cleaning
After getting the Data Set, the first step is cleaning data. Data cleaning is the process of preparing raw data for analysis by removing bad data, organizing the raw data, and filling in the null values. But we don’t have any need to clean Data because our data set is already cleaned. There are no missing values and unwanted columns or rows.
df.isnull().sum()
Nitrogen 0
Phosphorus 0
Potassium 0
temperature 0
humidity 0
ph 0
rainfall 0
label 0
Performed Some EDA
#lets count the crops values present in data
df['label'].value_counts()
Output:-
rice 100
maize 100
jute 100
cotton 100
coconut 100
papaya 100
orange 100
apple 100
muskmelon 100
watermelon 100
grapes 100
mango 100
banana 100
pomegranate 100
lentil 100
blackgram 100
mungbean 100
mothbeans 100
pigeonpeas 100
kidneybeans 100
chickpea 100
coffee 100
@interact
def summary(conditions=['Nitrogen','Phosphorus','Potassium','temperature',
'humidity','ph','rainfall']):
print(df.groupby(['label']).agg({conditions : ['mean','max','min']}))
@interact
def summary(conditions=['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',
'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',
'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',
'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee']):
a=df[df['label']==conditions]
data={'min':a.min(),'max':a.max(),'mean':a.mean()}
d = pd.DataFrame(data)
print(d)
print("Summer Crops")
print(df[(df['temperature']>30)&(df['humidity']>50)]['label'].unique())
print("----------------------")
print("winter Crops")
print(df[(df['temperature']<20)&(df['humidity']>30)]['label'].unique())
print("----------------------")
print("Rainy Crops")
print(df[(df['rainfall']>200)&(df['humidity']>30)]['label'].unique())
Output:-
Summer Crops
['pigeonpeas' 'mothbeans' 'blackgram' 'mango' 'grapes' 'orange' 'papaya']
----------------------
winter Crops
['maize' 'pigeonpeas' 'lentil' 'pomegranate' 'grapes' 'orange']
----------------------
Rainy Crops
['rice' 'papaya' 'coconut']
print('crops which requires very high ratio of Nitrogen content in soil:', df[df['Nitrogen']>120]['label'].unique())
print('crops which requires very high ratio of Phosphorous content in soil:', df[df['Phosphorus']>100]['label'].unique())
print('crops which requires very high ratio of Potassium content in soil:', df[df['Potassium']>200]['label'].unique())
print('crops which requires very high rainfall:', df[df['rainfall']>200]['label'].unique())
print('crops which requires very low temperature:', df[df['temperature']<10]['label'].unique())
print('crops which requires very high temperature:', df[df['temperature']>40]['label'].unique())
print('crops which requires very low humidity:', df[df['humidity']<20]['label'].unique())
print('crops which requires very low ph:', df[df['ph']<4]['label'].unique())
print('crops which requires very high ph:', df[df['ph']>9]['label'].unique())
Output:-
crops which requires very high ratio of Nitrogen content in soil: ['cotton']
crops which requires very high ratio of Phosphorous content in soil: ['grapes' 'apple']
crops which requires very high ratio of Potassium content in soil: ['grapes' 'apple']
crops which requires very high rainfall: ['rice' 'papaya' 'coconut']
crops which requires very low temperature: ['grapes']
crops which requires very high temperature: ['grapes' 'papaya']
crops which requires very low humidity: ['chickpea' 'kidneybeans']
crops which requires very low ph: ['mothbeans']
crops which requires very high ph: ['mothbeans']
Distribution Graph
we can see that some crops require a small amount of Nitrogen, some require average, and some require large amounts. Some crops require a small amount of Phosphorous, some require average, and some require large amounts. Some crops require a small amount of Potassium, some require average, and some require large amounts. Some crops require low Temperature, some require average, and some require high. Some crops require low Humidity in soil, some require average, and some require high. Some crops require low PH levels and some require high PH levels. Some crops require low Rain Fall, some require average, and some require high.
Splitting Data for Model Traning and testing ``` x=df.drop([‘label’],axis=1) print(x.shape) y=df[‘label’] print(y.shape)
Output:- (2200, 7) (2200,)
from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=1)
>**Model Building**
from sklearn.linear_model import LogisticRegression log_model=LogisticRegression() log_model.fit(x_train,y_train)
pred = log_model.predict(x_test)
>**Confusion Matrix**
from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test,pred)
print (“Confusion Matrix : \n”, cm)
Output:-
Confusion Matrix : [[22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 21 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 1 0 0 2 0] [ 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 1 2 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 1 0 0 23 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 1 0 0 0 23 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 12 0 0 1 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0] [ 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 11 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19]]
>**Model Accuarcy**
from sklearn.metrics import accuracy_score print (“Accuracy : “, accuracy_score(y_test,pred))
Output:- Accuracy : 0.9704545454545455
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