This document discusses using machine learning for agriculture applications. It introduces linear regression, including definitions and measurements like mean absolute error, mean squared error, and R2 metric. It then demonstrates how to use Google Colab notebooks and Python code to perform linear regression on crop yield data. Code examples are provided for loading and preparing data, fitting a linear regression model, making predictions, and calculating error metrics. Cross-validation is also demonstrated to evaluate model performance.

1. Machine Learning in Agriculture
Module 3
Presented By
Dr. Prasenjit Dey,
Assistant Professor,
Coochbehar Government Engineering College,
West Bengal, India

2. Linear Regression
 Definition
 Linear regression is a linear model, e.g., a model that assumes a linear relationship
between the input variable (x) and the output variable (y)
 f(x) = w1x1 + w2x2 + w3x3 + … + wnxn + C
 F(TLL,SD) = w1TLL + w2SD + C
 In higher dimension, when we have more than one input(x), the line becomes
plane or hyperplane

3. Linear Regression Measurements
 Mean Absolute Error (MAE)
 It is the sum of the absolute differences between predictions and actual values. It gives an idea of how wrong the predictions
were
 MAE = |y_predict –y_desired|
 Mean Squared Error (MSE)
 It is the sum of squared differences between predictions and actual values
 MSE = (y_predict –y_desired)2
 Root Mean Squared Error (RMSE)
 It is the squared root of the MSE, converts the units back to the original units of the output variable
 𝑅𝑀𝑆𝐸 = √(y_predict –y_desired)2
 R2 Metric
 It is an indicator of the goodness of a set of predictions to the actual values, lies between 0 and 1 for non-fit and perfect-fit
respectively
 R2 = 1 -
𝑆𝑢𝑚 𝑜𝑓 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑜𝑟 𝑒𝑟𝑟𝑜𝑟
𝑠𝑢𝑚 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑒𝑟𝑟𝑜𝑟

4. Using Python Notebook for Machine
Learning
 https://colab.research.google.com/notebooks/welcome.ipynb
 Go to file and upload your notebook(code file)
 Setting Up Google Python NoteBook

5. Coding
# conventional way to import pandas
 import pandas as pd
# read CSV file directly from a URL and save the results
 #data = pd.read_csv('https://raw.githubusercontent.com/Prasenjit123/ML-
Training/main/crop_yield.csv')
 data = pd.read_csv('https://raw.githubusercontent.com/Prasenjit123/ML-
Training/main/crop_data.csv')
# display the first 5 rows
 data.head()

6. Coding
# create a Python list of feature names
 feature_cols = ['TLL', 'SD']
# use the list to select a subset of the original DataFrame
 X = data[feature_cols]
# print the first 5 rows
 print(X.head())
# select a Series from the DataFrame
 y = data[Yield']
# print the first 5 values
 print(y.head())

7. Coding
# import model
 from sklearn.linear_model import LinearRegression
 from sklearn. model_selection import train_test_split
 from sklearn import datasets, linear_model
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)
# instantiate
 linreg = LinearRegression(normalize=True)
# fit the model to the training data (learn the coefficients)
 linreg.fit(X_train, y_train)

8. Coding
 from sklearn import metrics
 import numpy as np
# make predictions on the testing set
 y_pred = linreg.predict(X_test)
 for mp, av in zip(y_pred, y_test):
 print ('Model Predicted Value ={}, Known Value = {}, difference = {}'.format(round(mp,2),roun
d(av,2),round(abs(mp-av),2)))

9. Coding
 from sklearn import metrics
 print ('Mean Absolute Error = ', metrics.mean_absolute_error(y_test, y_pred))
 print ('Mean Squared Error = ',metrics.mean_squared_error(y_test, y_pred))
 print ('Root Mean Squared Error = ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
 print ('R2 score = ', metrics.r2_score(y_test, y_pred))

10. Coding
 from sklearn. model_selection import train_test_split
 from sklearn import datasets, linear_model
 from sklearn. model_selection import cross_val_score
 lm = linear_model.LinearRegression()
 scores = cross_val_score(lm, X, y, cv=10, scoring='r2')
 print("n", scores)
 print ("The mean of r2 is", scores.mean())
 scores = cross_val_score(lm, X, y, cv=10, scoring='neg_mean_absolute_error')
 print("n",scores)
 print ("The mean of absolute error is", scores.mean())

11. Coding
# read CSV file
 df = pd.read_csv('https://raw.githubusercontent.com/Prasenjit123/ML-
Training/main/crop_data.csv')
 print(df)
# create a Python list of feature names
 feature_cols = ['TLL', 'SD']
# use the list to select a subset of the original DataFrame
 input_data = df[feature_cols]
# make predictions on the testing set
 y_pred = linreg.predict(input_data)

12. Thank You