Agriculture assumes a predominant job in the development of the nation's economy. In the Myanmar economy, agriculture is the fundamental help and the major financial division of the nation. The atmosphere and other natural changes have become a significant danger in the agribusiness field. Machine Learning is a fundamental methodology for accomplishing pragmatic and powerful answers to this issue. Oil Crop Yield Prediction includes foreseeing the yield of the oil crop from accessible chronicled accessible information like climate boundary, soil boundary, and noteworthy harvest yield. This paper focuses on foreseeing the yield of the harvest depends on the current information by utilizing a machine learning algorithm. Real data of the Magway region were utilized for building the models and the models were tested with samples. The prediction will assist the rancher in predicting the yield of the crop before developing onto the agriculture field. To anticipate the crop yield in future precisely machine learning tools, a generally incredible and well known managed ML algorithm is utilized. This research is executed as a framework to anticipate crop yield from past information. This is accomplished by applying three machine learning techniques, for example, Neural Networks, Support Vector Machines, and Decision Tree strategies for oil crop information in the Magway region. Oil crops in the Magway district are sesame, groundnut, and some sunflower. The exploration is expected to assist farmers in predicting the yield of the crop before developing onto the agribusiness field. The proposed framework is intended to arrive at smart farming for Myanmar agriculture.

1. Comparison Analysis of Oil Crop Yield Prediction in
Magway Region Using Machine Learning Method
Presented by
Ei Ei Moe Tun
Associate Professor
University of Computer Studies, Mandalay

2. 2
Outlines
• Abstract
• Objectives of the Propose Works
• Introduction
• Background Theory
• Proposed System Architecture
• Data Preprocessing
• Data Classification with Three Methods
• Compare Classification Accuracy
• Conclusion
• Future Works
• References

3. 3
Abstract
• In Myanmar economy, agriculture is an important factor for farmer. Today trend is
going toward the smart farming for agriculture all over the world.
• Machine Learning is a fundamental methodology for accomplishing pragmatic and
powerful answers to this issue. Machine learning is an important decision support
tool for crop yield prediction, including supporting decisions on what crops to grow
and what to do during the growing season of the crops.
• Oil Crop Yield Prediction includes foreseeing the yield of the oil crop from
accessible chronicled accessible information like climate boundary, soil boundary,
and noteworthy harvest yield.
• This paper focuses on foreseeing the yield of the harvest depends on the current
information by utilizing three machine learning algorithm.
• Real data of the Magway region were utilized for building the models and the
models were tested with samples.

4. 4
Abstract (con’t)
• This is accomplished by applying three machine learning techniques, for
example, Neural Networks, Support Vector Machines, and Decision Tree
strategies for oil crop information in the Magway region.
• Oil crops in the Magway district are sesame, groundnut, and some sunflower.
• The exploration is expected to assist farmers in predicting the yield of the crop
before developing onto the agribusiness field.
• The proposed framework is intended to arrive at smart farming for Myanmar
agriculture.
• The prediction will assist the rancher in predicting the yield of the crop before
developing onto the agriculture field.
• To anticipate the crop yield in future precisely machine learning tools, a
generally incredible and well known managed ML algorithm is utilized.

5. 5
Objectives of the Propose Works
•To achieve maximum oil crops (sesame, groundnut and some sunflower) yields and
effective fertilizer usage for next five year based on average parameters like rainfall,
temperature, fertilizers, pesticides, ph level, and other atmospheric conditions and
parameters in Magway region.
•To alert the farmer for the minimum crops yields is caused by over usage of
fertilizers.
•To assist the farmer and agronomist who use the wrong usage of fertilizers that may
cause the health hazard.

6. 6
Introduction
• Machine learning, which is a branch of Artificial Intelligence (AI) focusing on
learning, is a practical approach that can provide better yield prediction based on
several features.
• Machine learning (ML) can determine patterns and correlations and discover
knowledge from datasets.
• The models need to be trained using datasets, where the outcomes are represented
based on past experience.
• The predictive model is built using several features, and as such, parameters of the
models are determined using historical data during the training phase.
• For the testing phase, part of the historical data that has not been used for training is
used for the performance evaluation purpose.
• Prediction is the methodology to predict the yield of the crops using different
parameters like rainfall, temperature, fertilizers, pesticides, ph level, and other
atmospheric conditions and parameters.
• This paper is used to monitor and predict crop yields in remote areas and cities around
Magway region.

7. 7
Background Theory
• Artificial Neural Network (ANN)
• Decision Tree
• Support Vector Machine

8. 8
Artificial Neural Network (ANN)
• ANNs are versatile machine learning algorithms that can perform classification and regression
tasks, and even multivariate output tasks.
• The model of a single artificial neuron can be understood in very similar terms to the biological
model.
• The figure 1 shows the multilayer neural network with one input layer, one hidden layer, and one
output layer.
• Activation functions are used to get precise output.
• By utilizing the ANN algorithm for oil crop yield helps to ensure the precision and effectiveness
of crop yield for maximizing crop production.
Fig 1. Multilayer Neural Network

9. 9
Decision Tree
• Decision trees are used in data mining and machine learning.
• Decision trees are the most popular machine learning algorithm because of their
intelligence and simplicity.
• It is as often as possible utilized by the scientists to characterize the information.
• This work deals with the prediction of crops yield as high yield or low yield using
Decision tree algorithm.
• The result of decision tree was compared with neural network and support vector
machine classifier.
Fig 2. A simple decision tree

10. 10
Support Vector Machine
• Support Vector Machines (SVMs) are a particularly efficient and flexible type of computation.
• A support vector machine (SVM) is a relatively simple machine learning algorithm used for
classification.
• They are used not only for classification but also for prediction.
• Basically, SVM finds a hyperplane that creates a boundary between data types.
• The SVN algorithm is used for oil crop yield helps to ensure crop yield for maximizing crop
production.
Fig 3. The Model of the Support Vector Machine

11. 11
Proposed System Architecture

12. 12
Overview of Data set
• This research utilizes crop yield prediction strategies to forecast the appropriate crop at
the remote areas and cities around Magway region. Magway region has five districts.
Magway district, Minbu district, Pakokku district, Thayet district and Gangaw district.
• The oil crop data are collected around these districts. Nine year historical data of
Magway region is used to predict the oil crop yields and to help for farmer for usage of
fertilizer and health hazard.
• Big amount of oil crop data in Magway region is recorded because of the five number of
districts, the data elements in this districts and the observations are recorded yearly.
• Data were collected from 2010-2019 i.e. nine years are collected to analyze the oil crops
prediction in Magway region.
• These heterogeneous sources are stored the data as different format and different unit i.e.
one district stored the unit of temperature attribute as kelvin and other as C..
• These types of different format and different unit are integrated with same form and
format at the preprocessing step of the proposed system.

13. 13
DATASE DESCRIPTION
Variable Description
Year The year 2010-2019 of Magway districts oil crop data
Township 25 townships of Magway Division
District Five districts in Magway Division
Crops Plant like a sesame, peanut, sunflower
Area Agriculture plants region total area
Temperature Average temperature of each township in a year
Rainfall Average rainfall of each township in a year
Humidity Average humidity of each township in a year
Production The crop’s production in Tons
Yield Average yield per acre
Yield Class Excellent (E) very good (VG) good (G) poor (P).
TABLE I. DATASE DESCRIPTION

14. 14
SAMPLE DATASET
TABLE II SAMPLE DATASET

15. 15
Data Preprocessing
• In the pre-process, the data which is collected from the five districts of Magway
region. There are four step in data preprocessing step.
• In the first step, noise values are removed by using noise detection algorithm.
The data needs to be cleaned at this stage because the raw data contains missing
values.
• In the second step, repeated values are removed by estimating the similar
values in the stored data.
• Data canonicalization is implemented in the third step.
• The last fourth stage, the components are analyzed to understand the nutrients
in the name of fertilizer. This step is used to recommend the farmer and
agronomist.

16. 16
Data Classification with Three Methods
• The output of the data preprocessing step enter the data classification as input for
classify three classes using three machine learning methods.
• These three methods is used to predict the yield and classify the four class of crop
yield.
• To model the relationship between the conditional attributes and decision attribute
used in crop yields prediction and classification of yields class will use three
machine learning methods such as Neural Network, Decision Tree, and SVM.
• The implementation of the research is used R programming. In the future, we
predict the yields of oil crops in Magway region with the available attributes and
calculate the accuracy with the detail data of five districts at the Magway region.
• And then, we want to recommend the farmers for the usage of fertilizer and health
hazard with the classified result of three models.

17. 17
Compare Classification Accuracy
• The crop data set with 500 instances in three different machine learning methods was
run.
• The accuracy result is analyzed with 80% of data set as training data and the rest 20%
is testing data.
• The analysis results are shown in Table 3.
• Among these methods, the SVM method is the worst accuracy over the data set with
train 80% and test 20%. Sometimes, by using the training and testing is changed the
accuracy of the SVM is better than the other two methods.
• Finally, the system concluded that the overall good accuracy depends on the more
training data.
• Figure 4 shows the comparison result of the three methods by using bar chart with
training 80% and testing 20% with 500 instances of crops dataset.

18. 18
Compare Classification Accuracy(con’t)
ML Method Precision Recall F-Measure
Decision Tree 1.0 1.0 1.0
SVM 0.888 0.880 0.887
ANN 1.0 1.0 1.0
TABLE III. ACCURACY RESULTS BY USING THREE METHODS WITH
TRAINING 80% AND TESTING 20% IN CROPS INSTANCE 500

19. 19
Sample Comparison of Three Methods using Bar Chart
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
Decision Tree SVM ANN
Comparison of Three Methods
Precision Recall F-Measure
Fig 4. Sample Comparison of Three Methods for Training 80 % and Testing 20%Performance
Evaluation with 500 instances

20. 20
Compare Classification Accuracy(con’t)
• And then, the crop data set with 1000 instance in three different machine
learning methods was run.
• The accuracy test result is analyzed with 70% of data set as training data
and the rest 30% is testing data.
• The analysis results are shown in Table 4.
• Among these methods, the SVM method and ANN is the best accuracy over
the data set with train 70% and test 30%.
• Table 5 shows the analysis results of crops data set with 1000 instances in
training 80% and testing 20%.
• In this evaluation, the overall f-measure is the best for ANN and SVN
methods.
• Figure 5 shows the comparison result of the three methods by using line
graph with training 70% and testing 30% with 1000 instances of crops
dataset.

21. 21
Compare Classification Accuracy(con’t)
ML Method Precision Recall F-Measure
Decision Tree 0.887 0.88 0.88
SVM 1.0 1.0 1.0
ANN 1.0 1.0 1.0
TABLE IV. ACCURACY RESULTS BY USING THREE METHODS WITH
TRAINING 70% AND TESTING 30% IN CROPS INSTANCE 1000

22. 22
Compare Classification Accuracy(con’t)
ML Method Precision Recall F-Measure
Decision Tree 0.887 0.88 0.88
SVM 1.0 1.0 1.0
ANN 0.90 0.99 0.98
TABLE V. ACCURACY RESULTS BY USING THREE METHODS WITH
TRAINING 80% AND TESTING 20% IN CROPS INSTANCE 1000

23. 23
Sample Comparison of Three Methods using Line Chart
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
Decision Tree SVM ANN
Comparison of Three Methods
Precision Recall F-Measure
Fig 5. Sample Comparison of Three Methods for Training 70 % and Testing 30% Performance
Evaluation with 1000 instances

24. 24
Conclusion
• In this paper, a comparison for Neural Networks, Support Vector Machines, and
Decision Tree Classifier are shown for analysis of oil crop information in the
Magway region.
• This paper analyzes the crop yield production based on available oil crop data. The
ML was used to predict the crop yield for maximizing crop productivity.
• According to the resulting factors, the performance of Neural Networks and Decision
Tree is more accuracy and more precision than SVM Classifier for training 80% and
testing 20%.
• But the performance of ANN and SVM is more accuracy and more precision than
Decision Tree. for training 70% and testing 30% in 1000 oil crops instances.
• Finally, the system concluded that the overall good accuracy depends on the more
training data. Crop yield prediction is an important issue for farmers and
agronomists.

25. 25
Future Work
• This paper means to propose and actualize an information framework to
estimate the crop yield creation from the pool of gathered past information.
• In the future, this model would be implemented with much more efficient
technique such as IOT based smart farming system with more efficient data
set.
• And then, we want to manage the usage of fertilizer for farmer health.

26. 26
REFERENCES
• Teresa Priyanka, Pratishtha Soni, C. Malathy, Recureeived 05 November 2018 Revised: 23 November 2018 Accepted: 02
December 2018 “Agricultural Crop Yield Prediction Using Artificial Intelligence and Satellite Imagery”.J. Clerk
Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
• Prashant Govardhan, Rasika Korde, Rashi Lanjewar (December 2018) “Survey on Crop Yield Prediction Using Data
Mining Techniques”.
• Eissa Alreshidi1 (No. 5, 2019) “Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT)
and Artificial Intelligence (AI)”
• Mona Kadryb , Ayman E. Khedra , Ghada Walidb , International Conference on Communication, Management and
Information Technology (ICCMIT 2015) , “ Proposed framework for implementing data mining techniques to enhance
decisions in agriculture sector Applied case on Food Security Information Center Ministry of Agriculture, Egypt”.
• Prof. D.S. Zingade1, Omkar Buchade2, Nilesh Mehta3, Shubham Ghodekar4, Chandan Mehta5 (Volume 4, Special Issue
5, Dec.-2017) “Crop Prediction System using Machine Learning”
• Zainab Arshad, Sohail Masood Bhatti, Huma Tauseef and Arfan Jaffar, “Heart Sound Analysis for Abnormality
Detection”, Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.022160, Lahore College for Women
University, Lahore, Pakistan corresponding Author: Sohail Masood Bhatti, 16 September 2021.
• www.coursehero.com
• www.archive.org
• www. towardsdatascience.com
• www. medium.com

27. 27
Thanks for Attending my
presentation