Crop Yield Prediction using Machine Learning

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2844
Crop Yield Prediction using Machine Learning
Hrishikesh Kalbhor1, Hrishikesh Mashire2, Prasad Tilekar3, Prof. Shahin Makubhai4, Prof.
Gurunath Waghale5
Department of Computer Science and Engineering, MIT School of Engineering,
MIT Arts Design and Technology University, Pune, 412201, India
----------------------------------------------------------------------***----------------------------------------------------------------------
ABSTRACT
In today’s world the most important thing for living in the
Indian economy is Agriculture. Above 70% of the world's
population is likely to be dependent on agriculture. Many
crops are cultivated in India, with wheat being one of the
most important food grains cultivated and exported by this
country. It can thus be seen that wheat is a big part of the
Indian agricultural system and the economy of India.
Therefore, it is very important to maintain the steady
production of the above-stated crop. To handle the
segmentation of the system we use the crop predictive
model.
Planning for agriculture plays a major role in agro-based
countries' economic development and food security. In
agricultural planning, the selection of crops is a significant
question.
KEYWORDS – Crop recommendation, pre-processing,
classifier algorithm, feature extraction, Machine Learning,
NLP (Natural Language Processing)
INTRODUCTION
For Predictive Analysis In order to increase productivity
and crop production efficiency, agricultural systems are
very efficient. Population, however, increases slowly, while
the crop production resource declines day by day.
Traditionally, farming includes planting the crop or
harvesting it according to a predetermined timetable.
Precision agriculture requires the collection of real-time
weather data, air quality, soil, crop maturity, machinery,
labour costs and current data availability. In this digital
world we use smarter decision process for solving our
problem. Therefore we use a predictive model in the sector
of agriculture.
Farmers, through their experience, predict the crop
production or yield; however this is also not the correct
approach. Oculus observation by consultants is the most
adopted method for the prediction and identification of
plant or crop yield.
OBJECTIVE:
● The reason for this decline in the agriculture
sector is due to the fact that farmers are not
empowered and due to lack of application of IT in
the farming sector.
● Farmers have inappropriate knowledge about
different types of crops and the climatic change.
● We tend to overcome this obstacle by applying
machine learning techniques to predict the crop
yield and name by considering various factors
such as temperature, rainfall, Season and area.
RELATED WORK OR LITERATURE SURVEY
[1] “Machine learning approach for forecasting crop yield
based on climatic parameters “
Author: S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh
Climate plays an important role in the field of agriculture.
Over this year due to increase in global warming climate
has been affected badly and it had a great impact on crops.
Predicting the crop yield will tell the farmers what to
harvest depending upon the the predictive analysis.
[2] Crop Selection Method to Maximize Crop Yield Rate
using Machine Learning Technique
Author: Rakesh Kumar1, M.P. Singh2, Prabhat Kumar 3
and J.P. Singh
Food Security and Economic Growth are one of the
important factors in the field of Agriculture across the
agro-based countries. Crop Selection is a difficult task for
agriculture planning depending on the climate. It depends
on various aspects such as climatic conditions, Market
price, production rate and Government policy.
[3] Prediction of crop yield and fertilizer recommendation
using machine learning algorithms

Author: Devdatta A. Bondre,
Mr. Santosh Mahagaonkar
Machine learning is an emerging research field in crop
yield analysis. Yield prediction is a very important issue in
agriculture. Any farmer is interested in knowing how much
yield he is about to expect. Over the decades Farmers
were predicting the crop based on their experience and
overall estimation of a particular crop.
[4] Agro consultant: intelligent crop recommendation
system using machine learning algorithms
Author: Zeel Doshi,Subhash Nadkarni
Agriculture is the main backbone of the Indian economy.
The Indian population depends either internally or
externally on agriculture for their livelihood. Thus,
agriculture plays a key role in the country. Many of the
farmers believe in harvesting crops by considering some
major factors like guessing, seasonal factors and their
previous experience.
I. MATHEMATICAL MODELING
Where,
Q = User entered input
CB = Pre-process
C = Feature selection
PR = Pre-process request evaluation
UB = Predict outcome
Set Theory
1) Let S be as system which input image
S = {In, P, Op, }
2) Identify Input In as
In = {Q}
Where,
Q = User entered input (dataset)
3) Identify Process P as
P = {CB, C, PR}
Where,
CB = Pre-process
C = Feature selection
PR = Pre-process request evaluation
4) Identify Output Op as
Op = {UB}
Where,
UB = Predict outcome
=Failures and Success conditions.
Failures:
1. Huge databases can lead to more time
consumption to get the information.
2. Hardware failure.
3. Software failure.
Success:
1. Search the required information available in
Datasets.
2. Users get the result very fast by giving their
appropriate inputs.
Space Complexity:
The space complexity depends on Presentation and
visualization of discovered patterns. More the storage of
data, the more the space complexity.
Time Complexity:
Check No. of patterns available in the datasets= n
If the given dataset is greater than one then fetching
information can be time consuming. Time complexity of
the given algorithm = O( )
Above mathematical model is NP-Complete.

EXISTING SYSTEM AND DISADVANTAGES
In the existing system there is no computerised system to
identify the crop recommendation and behaviour analysis.
Firstly, it is only suitable for the instance-level approaches
that require an instance classiﬁer, As we mentioned
before, existing popular approaches with neural networks
treat separated instances as inputs, then use a deep neural
network to transform them into embedding space.
ADVANCED SYSTEM AND ADVANTAGES
The proposed system because environmental variables
fluctuate by region, a machine learning model is employed
to estimate the optimal crop type for the chosen plot of
land. Machine learning techniques are used to pick the best
crop to cultivate with the highest likelihood of growing
using data from the standard dataset to train the crop
suggesting model. The optimal crop type is chosen using
XGBoot, Naive Bayes, and Support vector machine
methods. It was chosen what type of crops the farmer
should cultivate based on this model. Humidity,
temperature, soil moisture, pH level, and sunlight are all
aspects to consider.
Figure: Advance System Architecture
Advantages:
● Secure and efficient system.
● The advantage of this system in the field
of agriculture is that we can select proper
crops according to climate, temperature
etc.
● As Machine Learning helps us to make
predictions using the given data it avoids
assumptions and difficulties of using
larger sample spaces and complex
problems
FRONT-END
C
R
O
P
P
R
E
D
I
C
T
I
O
N
(LOGIN)
INPUT

CONCLUSION
According to our report, the scope is still open for the
Outcome enhancement. During the research that we
carried out, It is noted that the algorithm used for most of
the A unified approach is not used by writers where all the
variables are involved. It is possible to use the effect on
crop yield simultaneously to estimate crop yield. As the
dataset is considered to be limited in certain situations,
there is also more space for development. The outcome
can also be strengthened by using a large dataset
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