Bulletin of TUIT: Management and Communication Technologies
The Implementation of Machine Learning and Deep Learning Algorithms for Crop Yield Prediction
in Agriculture
Nodir Rahimov 1
1
Software Engineering, Tashkent University of Information Technologies, nikobek82@gmail.com
Dilmurod Khasanov 2
2
Software Engineering, Tashkent University of Information Technologies, tatusf2015@gmail.com
Abstract. In most Asian countries, since economy of the country rely on agriculture, in such countries, the
agricultural system is one of the most important sectors. Crop yield prediction is a crucial task in agriculture
that can help farmers make informed decisions and optimize their crop production. Accurate predictions can
help farmers better plan their resources and reduce waste, ultimately leading to higher profits and a more
sustainable agricultural industry. This article presents a comprehensive study on the utilization of machine
learning and deep learning techniques to predict the crop yield in agriculture, implemented and compared
some AI algorithms based on a given dataset. To this end, dynamic analyses data have been collected for crop
yield prediction and used to construct a regression prediction model using a multivariate regression (MR), a
deep neural network (DNN), multiple linear regression (MLR), gradient boosting regressor tree (GBRT) to
analyze a range of agricultural factors that impact wheat crop yields. These factors include soil moisture,
temperature, rainfall, and crop growth stages. The model is trained on a large dataset of wheat crop yields and
corresponding agricultural factors, allowing it to learn patterns and make accurate predictions. The
experiments conducted on the dataset demonstrate the effectiveness of the proposed model. The model
outperforms traditional statistical methods for crop yield prediction and achieves an accuracy of up to 90%.
The results show that the use of both deep learning and machine learning techniques can significantly improve
the accuracy of crop yield prediction in agriculture. The proposed approach has the potential to revolutionize
the agricultural industry by providing farmers and agricultural organizations with a more accurate and efficient
means of predicting crop yields. This, in turn, can help reduce waste and optimize resources, leading to a more
sustainable and profitable agricultural industry. The model can be integrated into existing agricultural systems
and can be used to make timely and informed decisions about crop management.
Keywords: machine learning, deep learning, MR, MLR, DNN,GBRT, gradient descent.
Introduction. Artificial intelligence (AI) has
become a crucial technology in the Fourth
Industrial
Revolution,
gaining
substantial
recognition in various domains such as finance,
healthcare, and manufacturing. The subfields of AI,
specifically machine learning and deep learning,
have become widely prevalent in diverse areas,
including speech recognition, computer vision,
language models, and industrial fault diagnosis [1].
Consequently, AI has attracted significant attention
as a revolutionary force capable of driving these
fields forward and enhancing human abilities,
presenting enormous potential for industry
transformation.
Given the critical role of agriculture in the
global economy, understanding global crop yield
patterns is essential for addressing food security
challenges and mitigating the impact of climate
change amid a growing human population.
Accurately predicting crop yields is a significant
agricultural challenge that depends on multiple
factors such as weather conditions (e.g., rainfall,
temperature) and pesticide application. Therefore,
having precise knowledge of crop yield history is
crucial when making decisions related to
agricultural risk management and yield forecasting
[1][2]. Crop yield prediction poses a challenge for
decision-makers at various levels, from global to
local scales. Farmers, for instance, can leverage
reliable crop yield prediction models to determine
optimal planting schedules and crop selection.
There are various approaches to forecasting crop
Nodir Rahimov, Dilmurod Khasanov
2023.Vol-2(9)
Bulletin of TUIT: Management and Communication Technologies
yields [2].
Machine learning represents a practical
approach that can facilitate improved crop yield
prediction by leveraging multiple attributes. As a
subdivision of Artificial Intelligence (AI) that
emphasizes learning, machine learning (ML) is
capable of extracting insights from datasets by
identifying correlations and patterns. During the
training phase, ML models are trained using
datasets that capture prior experiential outcomes,
and the resulting predictive models incorporate a
range of features and parameters calculated from
previous data. During the testing phase, unused
historical data is employed to evaluate model
performance. Depending on the research question
and topic, ML models can be descriptive or
predictive. Predictive models leverage past data to
forecast future events, while descriptive models
help to characterize current conditions or historical
trends. Machine learning techniques have been
instrumental in improving crop yield prediction and
crop management decision-making. In recent years,
a range of machine learning algorithms such as
multivariate regression, decision trees, association
rule mining, and artificial neural networks have
been deployed to enhance crop yield forecasting in
agriculture [5] [6].
This paper's primary contributions are as follows:
1. The primary objective of this study is to
compare the performance of various
machine learning and deep learning models,
including the multivariate regression (MR),
deep neural network (DNN), and multiple
linear regression (MLR), in predicting the
wheat yield for the next season using
previously collected data.
2. We provide a detailed description of the
data preprocessing procedure used in our
study. This process includes importing raw
data and constructing a dataset that includes
soil moisture, rainfall, temperature, and
volume of minerals (Nitrogen, Phosphorus,
natural minerals). Additionally, we remove
unnecessary data and classify the
specifications for model training and
validation.
3. To validate and evaluate the effectiveness
of our study, we conduct a comprehensive
comparative analysis of various models
used to predict crop yield for the next
season. This analysis includes an
assessment of the accuracy of these models,
allowing us to determine which approach is
most effective in accurately predicting crop
yield.
1. Related works
In recent years, there has been significant
research interest and activity focused on the topic
of crop yield prediction. Numerous studies have
been conducted in this area, exploring various
techniques and methodologies for predicting crop
yields with greater accuracy and precision. Koirala
et al. (2019) reviewed the use of Deep Learning
methods for fruit counting and estimating yield.
They revealed the ability of Deep Learning
methods to extract important features while
recommending approaches such as CNN detectors,
deep regression, and LSTM for estimating the fruit
load [3]. Dharani et al. (2021) conducted a review
on crop yield prediction using Deep Learning and
found that hybrid networks and RNN-LSTM
networks outperformed other networks. The
superior performance of RNN and LSTM can be
attributed to their storage and feedback loop
capabilities, enabling them to make accurate
predictions with time-series data on crop yield [4].
In their study on crop yield prediction using
Machine Learning, van Klompenburg et al. (2020)
found that neural networks, specifically CNN,
LSTM, and DNN, were the most commonly used
models. They also noted that the number of features
used varied depending on the study and that in
some cases, yield prediction relied on object
counting and detection instead of tabular data [5].
Amit et al. proposed their model that predicts
winter crop yield of wheat using DNN,
convolutional neural network(CNN) and XGboost.
Their proposed CNN model outperformed all other
baseline models used for winter wheat yield
prediction (7 to 14% lower RMSE, 3 to 15% lower
MAE, and 4 to 50% higher correlation coefficient
than the best performing baseline across test data)
[2].
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2023.Vol-2(9)
Bulletin of TUIT: Management and Communication Technologies
Table 1. Targets and methods of related works [6] .
Reference
Koirala et al. (2019)
Target
Method
Fruit detection for yield Convolutional Neural Network
estimation
(CNN), Long Short-Term Memory
(LSTM)
Dharani et al. (2021)
Crop prediction using deep Convolutional Neural Network
learning techniques
(CNN), Recurrent Neural Network
(RNN), Long Short-Term Memory
(LSTM)
van Klompenburg et al. Crop yield prediction with Long
Short-Term
Memory
(2020)
machine learning
(LSTM), Deep Neural Network
(DNN)
Amit et al.(2022)
Winter
wheat
prediction
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
yield Convolutional Neural Network
(CNN)
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 (X1, X2, X3, ...) and a single or
multiple dependent variables (Y1, Y2, Y3, ...) by
fitting a linear equation to the data. The general form
of the multivariate regression equation is as follows:
Y = β0 + β1X1 + β2X2 + β3X3 + ... + ε
where Y is the dependent variable, X1, X2, X3, ... are
the independent variables, β0 is the intercept or
constant term, β1, β2, β3, ... 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 (β1,
β2, β3, ...) 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
Nodir Rahimov, Dilmurod Khasanov
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Bulletin of TUIT: Management and Communication Technologies
values of the dependent variable and the actual
values of the dependent variable. In multivariate
regression, the number of independent variables can
vary, and the number of dependent variables can be
more than one. In cases where there are multiple
dependent variables, the regression equation takes
the form:
Y1 = β01 + β11X1 + β12X2 + β13X3 + ... + ε1
Y2 = β02 + β21X1 + β22X2 + β23X3 + ... + ε2
Y3 = β03 + β31X1 + β32X2 + β33X3 + ... + ε3
...
Yn = β0n + βn1X1 + βn2X2 + βn3X3 + ... + εn
where Y1, Y2, Y3, ..., Yn are the n dependent
variables, X1, X2, X3, ... are the independent
variables, β01, β11, β12, β13, ..., βn1, βn2, βn3, ... are the
coefficients or regression weights, and ε1, ε2, ε3, ...,
εn are the error terms.
Overall, the structure of multivariate
regression involves fitting a linear equation to the
data to model the relationship between multiple
independent variables and a single or multiple
dependent variables, and estimating the coefficients
using the OLS regression method.
2.2. Multiple Linear Regression (MLR)
Multiple linear regression (MLR), also referred
to as multiple regression, is a statistical approach that
employs several explanatory variables to forecast the
outcome of a response variable. The objective of
MLR is to establish a linear relationship between the
independent or explanatory variables and dependent
or response variables. Essentially, multiple
regression is an extension of ordinary least-squares
(OLS) regression, as it involves more than one
explanatory variable [8].
In the context of publishing an article, MLR can
be a powerful tool for analyzing data and drawing
conclusions that are supported by statistical evidence.
For example, MLR can be used to investigate the
relationship between various demographic factors
and a specific health outcome, or to analyze the
relationship between different types of marketing
strategies and sales outcomes. To use MLR
effectively, researchers must carefully choose their
independent and dependent variables and ensure that
they are measuring each variable accurately and
consistently. They must also ensure that they have a
sufficient sample size to achieve statistically
significant results. Once the data is collected,
researchers can use MLR to determine the strength
and direction of the relationships between the
independent variables and the dependent variable.
They can also use MLR to create predictive models
that can be used to estimate the value of the
dependent variable based on specific values of the
independent variables [8].
Multiple linear regression is a statistical method
that aims to model the relationship between a
dependent variable and multiple independent
variables. The structure of a multiple linear
regression model can be represented as follows:
Y = β0 + β1X1 + β2X2 + ... + βnXn + ε
Where:
Y is the dependent variable;
X1, X2, ..., Xn are the independent variables;
β0 is the intercept or constant term;
β1, β2, ..., βn are the regression coefficients, which
represent the expected change in Y for a one-unit
change in X1, X2, ..., Xn, while holding all other
independent variables constant;
ε is the error term, which represents the unexplained
variability in Y that is not accounted for by the
independent variables;
The multiple linear regression model aims to
estimate the values of the regression coefficients that
best fit the data, in order to make predictions about
the dependent variable based on the independent
variables. The quality of the model fit can be
assessed using measures such as the R-squared value,
which indicates the proportion of variance in the
dependent variable that is explained by the
independent variables [8].
In practice, multiple linear regression
models can be complex and may involve interactions
or nonlinear relationships between the independent
variables and the dependent variable. However, the
basic structure remains the same, with the aim of
modeling and predicting the relationship between a
Nodir Rahimov, Dilmurod Khasanov
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dependent variable and multiple independent
variables.
2.3. Deep Neural Network (DNN)
A deep neural network (DNN) is a particular
type of artificial neural network (ANN) that includes
multiple hidden layers situated between the input
and output layers, as depicted in Figure 1. The
process of learning in DNNs involves a repetitive
error backpropagation procedure, which modifies
weights to minimize the loss function's value
through optimization functions such as pure
propagation and stochastic gradient descent [1].
Nonetheless, increasing the depth of a neural
network can lead to gradient vanishing or exploding,
while increasing the number of neurons may lead to
overfitting. To tackle the issue of gradient vanishing
or exploding, an appropriate weight initialization
technique that is based on the type of activation
function can be employed. Additionally, overfitting
can be reduced by utilizing techniques such as
dropout and batch normalization. Furthermore,
advancements in hardware, such as improved
graphics processing units (GPUs), have significantly
reduced the computation time of complex matrices
in deep learning. DNNs that address these challenges
can perform complex nonlinear modeling. Therefore,
these techniques are highly effective in developing
highly accurate machine learning models capable of
handling complex, high-dimensional data. In
conclusion, DNNs are a powerful tool for addressing
complex machine learning problems, and their
ability to learn complex non-linear mappings from
high-dimensional data makes them highly effective
in various fields [1].
Figure 1. Construction of the deep neural
network (DNN) model [9].
2.4.Gradient Boosting Regressor Tree (GBRT)
Boosting is a type of ensemble machine learning
technique that combines multiple weak learners to
create a strong learner, as demonstrated in Figure 2.
Gradient boosting is one of the most popular and
commonly utilized boosting algorithms, which
focuses on improving the accuracy of the model by
enhancing the predictions made by prior models [1].
Figure 2. The typical structure of GBRT model
[16].
To start the gradient boosting algorithm, the first
model calculates the average prediction value of the
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target variables across the entire dataset and
technique that can significantly enhance the
prediction accuracy of machine learning models.
computes the residual. This residual is then utilized
to train multiple decision trees that create a stronger
3. Data preprocessing
model. The process of enhancing the model
iteratively continues by obtaining the gradient of the
Figure 2 illustrates the data preprocessing
residual and using it to reduce the residual even
process used for model learning. Initially, the dataset
further in the next model.
was imported into Python from kaggle.com.
Additional features were added to create a new
Gradient boosting has been found to be highly
dataset. Next, normalization was performed to
effective in improving the accuracy of machine
analyze the data. Finally, certain specification data
learning models [15-17]. It can be applied to a broad
were identified as model learning data, while the
range of data types and has been extensively utilized
Base specification data were set aside to evaluate the
to address regression problems. Therefore, gradient
performance of the generated predictive model.
boosting is a robust and powerful ensemble
Figure 2. The preprocessing process for prediction
crop yield data.
4. Results and discussion
4.1.Evaluation metrics
This study utilizes dataset that collected
during over 20 years, including measure of rainfall,
productivity of the each year, temperature. Machine
learning techniques, including a multivariate
regression (MR), deep neural networks (DNN), and
multiple linear regression predict, were employed
to construct the predictive model using Python,
Scikit-learn and Seaborn libraries. The predictive
performance of the models was evaluated using a
mean absolute error (MAE), and a root mean squared
error (RMSE), while a separate dataset was used to
test and verify the selected model. The test included
assessing the performance of each prediction model
on a separate dataset and generating graphs to
compare the predicted and actual values of crop yield
such as changing temperature, rainfall. For
regression problems MAE and RMSE metrics are
most implemented. In this section we compare the
results taken from four models through MAE and
RMSE according to mentioned four algorithms in
section 3.
MAE =
𝑛
1
∑ |𝑦𝑎𝑐𝑡 − 𝑦𝑝𝑟𝑒𝑑 |
𝑛
𝑖=1
∑𝑛𝑖=1(𝑦𝑎𝑐𝑡 − 𝑦𝑝𝑟𝑒𝑑 )2
√
RMSE =
𝑛
Where:
n
(1)
(2)
is the number of data points,
ypred
is the predicted value of the dependent
variable for the ith data point,
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Bulletin of TUIT: Management and Communication Technologies
based on the test dataset. The prediction results of the
yact is the actual value of the dependent variable for
th
MR model for the test dataset are visualized in
the i data point.
Figure 3 and
Figure 4.
4.2.Multivariate Regression Prediction Model
Table 2. The performance evaluation results of the MR
Performance
prediction model.
The performance evaluation of the predictive
model is presented in Table 2, where the MR model
exhibits RMSE values of 83256.2 and 84955.1,
and MAE values of 93365.8 and 64242.0 when
predicting the crop yield prediction, respectively,
Figure 3. High-correlation Figure 4. True values
among features.
(blue) and predictions
(orange).
4.3.Multiple Linear Regression Prediction Model
Performance
The performance evaluation of the predictive
model is presented in Table 3, where the MLR model
exhibits MAE values of 63879.3 and 64099.9,
and RMSE values of 84145.8 and 84254.6 when
predicting the crop yield prediction, respectively,
Figure 5. The dynamics Figure 6. True values
Metric
Target
Predictio
n
MAE
Train
Test
63365. 64242.
8
0
RMSE
Train
Test
83256. 84955.
2
1
based on the test dataset. The prediction results of the
MLR model for the test dataset are visualized in
Figure 5 and Figure 6.
Table 3. The performance evaluation results of the MLR
prediction model.
Metric
Target
Prediction
MAE
Train
Test
63879.3
of crop by years.
64099.9
RMSE
Train
Test
84145.8
84254.6
and predictions.
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Bulletin of TUIT: Management and Communication Technologies
in certain circumstances, MLR has been identified as
4.4.Deep Neural Network Prediction Model
the most optimal algorithm for predicting crop yield
Performance
based on the research findings.
The performance evaluation of the predictive
model is presented in Table 4, where the DNN model
exhibits MAE values of 63713.9 and 63747.2,
and RMSE values of 83510.5 and 83493.9 when
predicting the crop yield prediction, respectively,
based on the test dataset.
Table 4. The performance evaluation results of the DNN
prediction model.
Metric
Target
Prediction
MAE
Train
Test
63713.9
63747.2
RMSE
Train
Test
83510.5
The performance evaluation of the predictive
model is presented in Table 5, where the GBRT
model exhibits MAE values of 61378.7 and
61749.5, and RMSE values of 79139.3 and 79641.6
when predicting the crop yield prediction,
respectively, based on the test dataset.
Table 5. The performance evaluation results of the GBRT
prediction model.
83493.9
The study has revealed that multiple linear
regression (MLR) outperforms other algorithms that
were evaluated in terms of dataset size, sorting, and
key features. Although models based on deep neural
network (DNN) and multiple regression (MR)
algorithms have been observed to be highly effective
Metrics →
Models ↓
4.5. Gradient Boosting Regressor Tree Model
Performance
Root Mean squared error
(./1000 ha)
Metric
Target
Prediction
MAE
Train
Test
61378.7
61749.5
79641.6
Mean percentage
error (%)
MR
84.10
61.8
83
MLR
84.2
63.98
80
DNN
83.5
62.7
82
GBRT
70.39
59.5
88
In recent years, machine learning techniques,
such as multivariate linear regression (MLR),
multiple regression (MR), and deep neural networks
(DNN), have shown promising results in crop yield
prediction. In this paper, we evaluated the
performance of MLR, MR, and DNN models in
predicting crop yield using a publicly available
dataset. Our results show that GBRT outperforms
MLR,DNN and MR models in terms of prediction
accuracy, with lower mean absolute error (MAE)
and root mean squared error (RMSE) values. This
indicates that GBRT is better suited for modeling
79139.3
Table 6. The SOTA comparison of models.
Mean absolute error
(./1000 ha)
5. Conclusion
RMSE
Train
Test
multi-functional relationships between crop yield
and various environmental and management factors.
However, we also note that the choice of
algorithm for crop yield prediction depends on
several factors, including the complexity of the
problem, the amount and quality of data, and the
specific application requirements. While GBRT
may perform better in some cases, MLR, DNN and
MR models can be more interpretable and easier to
implement in certain scenarios.
In our future works, we aim to expand the
dataset by collecting more data with varying
specifications, including a new features for effecting
crops, creating new application to collect the
agricultural data from farmers, reducing range of
Nodir Rahimov, Dilmurod Khasanov
2023.Vol-2(9)
Bulletin of TUIT: Management and Communication Technologies
learning area (specific area from central Asia). By
11. Jiang, S.; Li, J.; Zhang, S.; Gu, Q.; Lu, C.;
doing so, we can improve the generalization and
Liu, H. Landslide risk prediction by using
prediction performance of the prediction model,
GBRT algorithm: Application of artificial
making it more effective in the real world.
intelligence in disaster prevention of energy
mining. Process. Saf. Environ. Prot. 2022,
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