The Implementation of Machine Learning and Deep Learning Algorithms for Crop Yield Prediction in Agriculture

 
Reference 
Target 
Method
Koirala et al. (2019) 
Fruit detection for yield 
estimation 
Convolutional Neural Network 
(CNN), Long Short-Term Memory 
(LSTM) 
Dharani et al. (2021) 
Crop prediction using deep 
learning techniques 
Convolutional Neural Network 
(CNN), Recurrent Neural Network 
(RNN),
Long Short-Term Memory 
(LSTM) 
van Klompenburg et al. 
(2020) 
Crop yield prediction with 
machine learning 
Long 
Short-Term 
Memory 
(LSTM), Deep Neural Network 
(DNN) 
Amit et al.(2022)
Winter 
wheat 
yield 
prediction 
Convolutional Neural Network 
(CNN) 
2. Methods 
2.1.Multivariate regression (MR) 
Multivariate regression is a statistical 
method that is widely used in various fields of 
research, such as economics, finance, psychology, 
and social sciences. The primary goal of multivariate 
regression is to model the relationship between 
multiple independent variables and a single or 
multiple dependent variables. This modeling is done 
by fitting a linear equation to the data, which allows 
for the prediction of the value of the dependent 
variable for any given combination of values of the 
independent variables. Multivariate regression is a 
more general statistical method than multiple linear 
regression, which focuses on modeling linear 
relationships between the dependent variable and 
two or more independent variables. In contrast, 
multivariate regression allows for the analysis of 
complex relationships between multiple variables 
that may not be linear and can account for 
correlations among the dependent variables. One of 
the significant advantages of multivariate regression 
is its ability to analyze the relationship between 
multiple variables simultaneously, which can lead to 
more accurate and robust results compared to 
analyzing each variable separately. For example, in 
economics, multivariate regression is used to model 
the relationship between multiple economic 
indicators, such as inflation, interest rates, and GDP, 
to predict the behaviour of the economy as a whole. 
Another advantage of multivariate regression is its 
ability to handle missing data and outliers, which can 
occur in real-world data. By considering multiple 
variables simultaneously, multivariate regression 
can better handle missing data and outliers, leading 
to more accurate results.
The structure of multivariate regression 
involves modeling the relationship between multiple 
independent variables (X
1
, X
2
, X
3
, ...) and a single or 
multiple dependent variables (Y
1
, Y
2
, Y
3
, ...) by 
fitting a linear equation to the data. The general form 
of the multivariate regression equation is as follows: 
Y = β
0
+ β
1
X
1
+ β
2
X
2
+ β
3
X
3
+ ... + ε 
where Y is the dependent variable, X
1
, X
2
, X
3
, ... are 
the independent variables, β0 is the intercept or 
constant term, β
1
, β
2
, β
3
, ... are the coefficients or 
regression weights that represent the impact of each 
independent variable on the dependent variable, and 
ε is the error term or residual. The coefficients (β
1
, 
β
2
, β
3
, ...) are estimated from the data using a method 
called ordinary least squares (OLS) regression, 
which minimizes the sum of the squared residuals to 
find the best-fitting line to the data. The OLS 
regression method finds the values of the coefficients 
that minimize the difference between the predicted 

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