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Journal of Development Economics and Management Research Studies (JDMS), A Peer Reviewed
Open Access International Journal, ISSN 2582 5119 (Online), 09 (11), 127-137, January-March, 2022
127
Crops Prediction Based on Environmental
Factors Using Machine Learning Algorithm
Journal of Development Economics and
Management Research Studies (JDMS)
A Peer-Reviewed Open Access
International Journal
ISSN: 2582 5119 (Online)
Crossref Prefix No: 10.53422
09(11), 127-137, January-March, 2022
@Center for Development Economic Studies
(CDES)
Reprints and permissions
https://www.cdes.org.in/
https://www.cdes.org.in/about-journal/
Crops Prediction Based on Environmental Factors Using Machine Learning
Algorithm
Dr. Poorna Shankar
1
, Dr. Prashant Pareek
2
, Ms. Urvi Patel
3
, and Mr. Canny Sen
4
Abstract
India is an agricultural country, much of the economy is dependent on productivity
growth. Agriculture is heavily dependent on rainwater and depends on various soil conditions,
namely nitrogen, phosphorus, potassium, and climates such as temperatures and rainfall. The
growth of agricultural technology will increase crop production. Machine learning is a
promising area for research to anticipate yield based on data patterns. The proposed learning
algorithms apply to the machine learning algorithms: Random Forest, Logistic Regression,
Decision Tree, and Support Vector Machine. Predictions of plants that are most relevant to the
current environment are being made. This work gives producers a strong prediction of planting
what types of crops in their area on the farm according to the above-mentioned parameters to
grow a smart agricultural product. four different algorithms are applied in this project system.
With the help of the ROC-AUC-SCORE, the accuracy of all the models is compared and other
factors like precision, recall, F1 score, and support are also compared. And from all these
results we can know which model is perfect and from that, we can know which crop is suitable
for the given soil and climatic condition.
Keywords: Crop Prediction, Logistics regression, K-means clustering, exploratory
analysis, Random Forest, Decision tree, Support vector machine.
1. Introduction
Ability to construct a single assessment model to indicate the most favourable plants to
grow based on current climate and soil conditions. Direct farming is the adoption of more
specialized methods that use technology to meet the needs of individual sites and plants. Direct
1
Professor, Keystone Global, Ahmedabad, Gujarat.
2
Assistant Professor, Shanti Business School, Ahmedabad, Gujarat.
3
Pursuing MSC in Big Data, FOM University, Germany.
4
Pursuing MSC in Big Data, FOM University, Germany
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agriculture helps farmers to live a debt-free life as there is less agriculture and losses and the
overall environmental impact is also lower. Direct farming will be used for this project. With
this technique, the farmer can estimate the yield. Thereby, the farmer can get a livelihood. India
is an agricultural country. So, if every farmer is healthy, the Indian economy will also be strong.
By using this method, the farmer does not know which crop is more profitable from the soil
and nature point of view. So, the farmer does not give up and the farmer earns good money so
that the farmer does not commit suicide and with this technology, we can reduce farmer suicide
cases. So, this project should be done keeping in view the situation of the farmer. The project
predicts high yields of low-cost crops. Provides a system that allows farmers to access relevant
information. This project will help to create an assessment model that will provide the most
effective solution. Determines additional variations in yield defined by soil and climatic
variations.
The agricultural sector is undergoing a transformation driven by new technologies,
which looks very promising, as it will enable us to take this major sector to the next level of
agricultural production and agriculture. Precision agriculture, when the necessary inputs are
needed and when needed, is the third wave of the modern agrarian revolution (the first is
mechanization and the second is the green revolution and genetic modification), and systems
are being improved by increasing agricultural knowledge these days. Availability of large
amounts of data. This research work is the result of a crop planning program to increase
productivity based on key technologies: the Internet of Things and Machine Learning
Techniques. Machine learning techniques make plant estimates based on data. The use of this
technology helps the farmer to get a better agricultural product. The main goal of agriculture
is to improve crop yields with low maintenance costs and low environmental pollution.
Potential growth and yield depend on many different production characteristics such as climate,
soil characteristics and irrigation and fertilizer management.
Agriculture is the backbone of any economy. In a country like India where food demand
is increasing due to population growth, there is a need to make progress in agriculture in line
with demand. Agriculture is considered to be the main and most important tradition practised
in India since ancient times. In this project, we will use the extensive database available on the
Coley website to create a model that can predict which crops will adapt to the given soil and
climate change. The database contains a variety of plant extracts, including temperature,
humidity, pH, precipitation, nitrogen (N), potassium (P), and phosphorus (K). After collecting
the data, we clean the data to remove the lost values and if there are any vendors find them and
manage them and we can do some data analysis to understand the data more deeply. We extract
the feature and simulate the estimate using feature engineering. The goal of this project is
precision farming. We need to improve productivity by understanding the climate and soil
requirements of plants. It helps us deal with unpredictable weather. Compilation and
development of crop yield estimates using data analytics to help increase yields and subsequent
profitability in agricultural production.
1.1 Machine Learning
Machine learning is the most widely used method in agricultural matters. It is used in
the analysis of big data collections and information categories. Creating useful fields and
patterns. The general purpose of the machine learning method is to obtain data from the data
collection then converts it into an understandable framework for continuous use. The main
purpose of this paper is to develop a system that can predict the type of plant depending on soil
and climate factors. As the population grows in today's world, as the years go by, this is
expected to be in the billions and we need increased product production to cater to those billions
of people. the population is growing, too, on the other hand, the agricultural sector is declining
Journal of Development Economics and Management Research Studies (JDMS), A Peer Reviewed
Open Access International Journal, ISSN 2582 5119 (Online), 09 (11), 127-137, January-March, 2022
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due to several factors. such as major industrial development, commercial market development
in residential buildings built on the farm; Therefore, supply with a growing number of
consumers, there is a need to improve actual production can be obtained by using the most
appropriate and most important object needed everyday life is clever farming.
2. Research Methodology
The research approach focuses on the latest technology that can be used to provide an
alternative to the current approach to agriculture. The study is divided into 3 sections. Section
1 deals with machine learning and precision agriculture, section 2 deals with outcomes and
consequences, section 3 deals with the future and scope of the future.
2.1 Machine learning cognitive technology
According to Mission Learning is a branch of artificial intelligence and computer
science responsible for the development of algorithms that represent independent learning
materials. With the help of accurate and efficient machine learning systems capable of
exploring a very broad set of tasks are developed to solve day-to-day tasks. Scientists can use
computer simulations to perform early plant experiments to determine how certain species can
adapt to different climates, soil types, climatic patterns, and more. These digital experiments
do not replace field testing, but allow plant growers to more accurately predict crop
performance.
Figure 1 Machine learning data processing diagram
Information about the plant species to be tested is used as input and transmission
through a surveyed, supervised or non-monitored machine learning system such as Random
Forest, Logistics regression, Decision tree and vector support machine etc. The method
analyses the input to extract relevant features and information related to the issue. Depending
on the variety and functions set, the processing algorithm performs the data analysis and
provides the output of possible split or retrospective output.
2.2 Impact of Precision Agriculture
Using agricultural understanding technology can help determine the best crop selection
for different climatic conditions and best suits the needs of farmers. This can be achieved by
analysing and comparing information about seed varieties, climate, soil types, specific attack’s
location, probability of disease and data about what worked best, annual results, current market
Journal of Development Economics and Management Research Studies (JDMS), A Peer Reviewed
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trends, prices and consumer needs. Farmers can then decide how to increase their crop yields.
The speed at which machine learning technology develops will show that the agricultural
industry is on the verge of technological change under artificial intelligence as its driving force.
2.3 Machine Learning Algorithm for Prediction
Machine learning is often used in agricultural problems. It is used in the analysis of big
data collections and information categories to create useful fields and patterns. The general
purpose of machine learning is to extract data from data collection and convert it into an
understandable framework for continuous use. Based on accessible information, this document
analyses the crop yield form. To increase crop production, a machine learning method was used
to predict crop yields. This reflects the distribution of the predicted yield forecast. As shown in
the above scenario, devices are used on the farm to obtain information connected to humidity,
temperature, rainfall and ph. Logistics Regression algorithms are used to identify data obtained.
The expected result indicates any soil that may be suitable for certain crops and the condition
of groundwater.
A. Overview of Data
We get information from various places and organize data sets in this category. And for
analytics, these data sets are used. Online tools such as Data.gov.in and Kaggle.com are also
used to create accurate information. Time shows that the height of the plant indicates the
temperature required for the plant in months, the minimum and the size of the feature. N, P, K
values are plant-specific fertilizers, low and high PH values, maximum and minimum rainfall
requires for crops and soil moisture levels. Use the data to estimate only for limited crops like
fruits, grains, beans etc. and the farmer must be financially strong to use this project or this
application. Also, for the weather, we should use only two factors namely temperature and
rainfall. For soil, we should use only certain elements like nitrogen (N), potassium (P),
phosphorus (K), moisture, pH etc. The dataset has 2,200 records and 8 attributes.
B. Logistics Regression
The logistic regression algorithm in ML is taken from the general logistic regression
model in statistics. In modelling yields from a cropping system based on agronomy (upper and
lower), the logistics function is estimated as follows:
In this case, we are modelling the potential for an input (X) (yield from four different
crop systems) class (Y = 1 (Highlands)), and we can formally write it as:
P(X) = P (Y = 1|X).
The logistic regression algorithm (LR) was used as the classification method (linear
method), but the estimates were changed using the logistic function. The function can be called:
C. Random Forest
Random Forest is a supervised learning algorithm used for both classification and
regression. The Random Forest Algorithm builds a decisive tree on different data models and
then estimates the data from each subset and provides a better solution for the system through
polling. Used the bagging method to train random forest data. The bagging method is to study
different models and improve the result of the system. We used a random forest algorithm to
Journal of Development Economics and Management Research Studies (JDMS), A Peer Reviewed
Open Access International Journal, ISSN 2582 5119 (Online), 09 (11), 127-137, January-March, 2022
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obtain high accuracy, which gives the accuracy estimated by the model and the actual results
of the estimate in the dataset. The decisive tree is formed from data and tree patterns in the
random forest provides an estimate from each family and select the best solution by voting to
provide better accuracy to the model. This will give the correct results for the system.
D. Decision Tree
The Decision Tree is a data structure constructed using nodes (values or conditions)
and margins (connecting all nodes). The decision tree is constructed based on a dataset that
contains the characteristics or features that characterize the raw data for each record. Each node
in a tree can be a decision-making node or a leaf node that delivers a result. The ID3 algorithm
before the C4.5 algorithm tries to build the decision tree as small as possible. It uses the entropy
of each feature to determine which edge to follow. Since ID3 is a greedy top-down approach,
it identifies the feature with extreme values (highest data gain features). It helps to identify
which features are from the most homogeneous branches.
E. Support Vector Machine
Like a popular machine learning algorithm, SVM is a new generation learning system.
Based on recent advances in statistical learning theory. This VC Absorbs the principle of the
magnitude and principle of the minimum structural risk to the objective function and then find
the partition hyperplane that can meet the square requirement. The important advantage of
SVM is that it can be theoretically analysed using cutting concepts from computational learning
theory and achieve cutting-edge performance. Recently, it has also been applied to several real-
world issues such as handwriting recognition, data retrieval, and biomedical data classification.
In this paper, SVM is introduced to classify farm data to improve farm data classification
performance.
3. Implementation
After collecting the features from the submitted data, we can easily estimate Depending
on the type of crop on the characteristics we consider. Almost After evaluating the effectiveness
and efficiency of our experiment, we can evaluate the appropriate crops for the given attributes.
3.1 Exploratory Data Analysis
We did exploratory data analysis to find the correlation of data and to understand how was
the data distributed.
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Figure 2 Distribution for Agricultural Conditions
These are distribution charts for all climate and soil conditions. From this chart, we
know that some crops require high amounts of phosphorus and potassium. Because the value
of phosphorus and potassium charts in the charts that have been found is very high. This chart
shows that some crops require very high and very low temperatures. And some crops require
less and more pH in the soil.
Figure 3 Correlation of attributes
We found the correlation between the data using a different combination of attributes and
graphs. Figure. 3 shows how each attribute is correlated to attributes.
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3.2 Classification Algorithm
After understanding data properly, we train our prediction algorithm one by one and obtain
precision, recall, F-score and accuracy. The random forest classifier showed excellent results
with an accuracy of 100%, whereas, the accuracy of Logistics regression was 97%, the
accuracy of the Decision tree was 99% and Linear Support Vector Classifier was 98%. Table
1 shows the error rate and accuracy of each algorithm.
Error rate
Accuracy
Random Forest
0.00
1.00
Logistics Regression
0.03
0.97
Decision Tree
0.01
0.99
Support Vector Machine
0.02
0.98
Table 1 Results of algorithms
Table 1 shows the accuracy Score of various classification Algorithms are compared and
the best algorithm based on the accuracy score is chosen for the system.
4. Results and Model Evaluation
With the help of this confusion matrix, we can know whether our model is accurate or
not. With the help of a classification report, we know the value of precision and recall. We can
call our model good only if the value of precision and recall of our model is good. In this
project, the value of recall and precision for 22 classes of our dataset is very high which means
that our model is very accurate.
Figure 4 Confusion matrix for Random Forest
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Figure 5 Confusion matrix for Logistics Regression
Figure 6 Confusion matrix for Support Vector Machine
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Figure 7 Confusion matrix for Decision Tree
Figure 8 shows a Graphical Representation of the Accuracy Score of various Classification
Algorithms.
Figure 8 Bar graph for Result Analysis
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5. Conclusion
Once this work is completed, we will be able to predict the plants according to the soil
and the recommended climate. The proposed plan lists all potential crops for a particular
region, helping the farmer to decide which crop to plant. The program has conducted a
comprehensive assessment of soil, climate and pH knowledge and shows which plants are most
beneficial to be grown in the right environment. Direct agriculture is still desirable in many
developing countries. It is possible to achieve the above vision in India to improve food security
and the individual income of farmers. The above-mentioned challenges and promising
solutions to predict the future of Indian agriculture. Technological advances and the
government's efforts to promote and promote agriculture with the right resources, assistance,
tax holidays and other benefits to farmers will greatly attract investment. This initiative will
therefore help deliberate efforts to prevent the growth and sustainability of future generations.
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