it is project title named SMART FARMING: DATA-DRIVEN CROP RECOMMENDATION SYSTEM

1. The 7 Habits of Highly Effective People
Be Proactive.
Begin with the end in mind.
Put first things first.
Think Win-Win.
First Understand, then be Understood.
Synergize.
Sharpen Your Saw.
Practicing at
VIGNANA JYOTHI GROUP OF INSTITUTIONS
We have followed the above 7 steps during our project work.
B Snehitha ROLL No: 20071A0465
P Uday Kiran Reddy ROLL No: 20071A0498
V Omkar ROLL No: 20071A04B6
V Jathin Sai ROLL No: 20071A04B8

2. SMART FARMING: DATA-DRIVEN CROP
RECOMMENDATION SYSTEM
A MINI PROJECT REPORT
BACHELOR OF TECHNOLOGY
IN
ELECTRONICS & COMMUNICATION ENGINEERING
Submitted By
UNDER THE SUPERVISION OF
G SAHITYA
ASSOCIATE PROFESSOR
VNRVJIET
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
VALLURUPALLI NAGESWARA RAO VIGNANA JYOTHI INSTITUTE OF
ENGINEERING & TECHNOLOGY
NBA Accredited CE, EEE, ME, ECE, CSE, EIE, IT -B.Tech Programs Approved by AICTE,
New Delhi, Affiliated to JNTUH
VIGNANA JYOTHI NAGAR, BACHUPALLY, NIZAMPET (S.O.), HYDERABAD
500090. TELANGANA, INDIA.
B Snehitha ROLL No: 20071A0465
P Uday Kiran Reddy ROLL No: 20071A0498
V Omkar ROLL No: 20071A04B6
V Jathin Sai ROLL No: 20071A04B8

3. ACKNOWLEDGEMENTS
We Indebted to Dr. C. D. NAIDU, Principal, VNRVJIET, for his help and guidance in
our work. We consider ourselves fortunate to have obtained his friendly and valuable advice
during the course of my research.
Our sincere thanks to Dr. S RAJENDRA PRASAD, Professor, Head of the Department,
ECE, VNR VJIET for his esteemed guidance and encouragement provided during the course of
our project.
We would like to express our sincere thanks to G SAHITYA, Associate Professor, VNR
VJIET for her precious guidance and kind co-operation at every step of this project work.
We thankful to all the staff members of ECE department, VNR VJIET for helping us
during this project.
We thankful to all the project committee members of ECE department, VNR VJIET for
helping us during this project.
Finally, we are very thankful to our family members and our friends for their great moral
support.
B Snehitha ROLL No: 20071A0465
P Uday Kiran Reddy ROLL No: 20071A0498
V Omkar ROLL No: 20071A04B6
V Jathin Sai ROLL No: 20071A04B8

4. 1
ABSTRACT
Machine learning techniques such as K-nearest neighbors (KNN) have the potential to greatly
impact the agricultural sector in India. By leveraging these advanced tools, farmers can benefit
from improved decision-making processes and enhanced agricultural practices. One area where
machine learning can provide significant advantages is in weather prediction. By analyzing
historical weather patterns and current meteorological data, algorithms can generate highly
accurate predictions for rainfall, temperature, and other weather parameters. This information
enables farmers to plan their planting and irrigation schedules more effectively, reducing the risk
of crop damage due to adverse weather conditions. Another important application of machine
learning in agriculture is pest and disease management. Algorithms can analyze various factors
such as crop health data, environmental conditions, and pest prevalence to identify early warning
signs of potential infestations or outbreaks.
Machine learning can also optimize resource management on farms. By analyzing soil
composition, nutrient levels, and water availability data, algorithms can generate customized
recommendations for fertilizer application and irrigation. This precision in resource management
helps reduce waste and ensures that crops receive the optimal amount of nutrients and water,
leading to improved yields and cost savings. Efficient farm machinery management is another area
where machine learning can provide benefits. Algorithms can analyze data on equipment usage,
fuel consumption, and maintenance history to identify patterns and optimize machinery utilization.
This reduces downtime, improves operational efficiency, and prolongs the lifespan of farm
equipment. Machine learning can also contribute to sustainable farming practices. With the ability
to analyze data on organic matter content, soil erosion, and crop rotation history, algorithms can
provide insights and recommendations for sustainable land management. This promotes soil
health, reduces environmental impact, and supports long-term agricultural sustainability.
Additionally, machine learning algorithms can assist in crop quality assessment. By analyzing data
on factors such as color, size, and shape, algorithms can evaluate and classify harvested crops
based on quality attributes. This can help farmers accurately sort and grade their produce, ensuring
that only the highest quality crops reach the market. Market forecasting is another valuable
application of machine learning in agriculture. This information empowers farmers to make
informed decisions regarding crop selection, timing of harvest, and pricing strategies.
In conclusion, machine learning technologies offer numerous opportunities to
improve agricultural practices in India. From weather prediction and pest management to resource
optimization and market forecasting, machine learning algorithms can enhance decision-making
processes, increase productivity, reduce waste, and promote sustainable farming practices. As
technological advancements continue, the integration of machine learning in agriculture holds
great promise for the future of the industry in India.

5. 2
INDEX
Chapter 1: Introduction
1.1 Introduction 4
1.2 Literature survey 5-6
Chapter 2: Methodology
2.1 Datasets 7
2.2 Algorithm Proposed 7-8
2.3 Block diagram 9
Chapter 3: Process & Requirements
3.1 Software Requirements
3.1.1 Jupyter Notebook 10
3.2 Hardware Requirements
3.2.1 Arduino Mega 2560 10
3.2.2 Soil NPK Sensor 11
3.2.3 Soil Moisture Sensor 12
3.2.4 DHT11 Sensor 13
3.2.5 LCD 13
Chapter 4: Results, Conclusion & Future Scope
4.1 Result 14-15
4.2 Conclusion 16
4.3 Future Scope 16
Chapter 5: Reference
5.1 References 17
s

6. 4
CHAPTER 1: INTRODUCTION
1.1 Introduction:
Agriculture is a critical sector that plays a vital role in feeding the growing global
population. However, many countries still face challenges in achieving food security and
eliminating hunger. To address these issues, it is essential to focus on enhancing food production
and ensuring sustainable agriculture practices. This project aims to contribute to these objectives
by focusing on crop protection, land assessment, and crop yield prediction. Using the Python
programming language and various libraries for data manipulation, machine learning, and
visualization, this project aims to provide farmers with valuable insights and recommendations for
crop selection and management. Through data manipulation techniques, the project will preprocess
and clean the dataset to make it suitable for analysis. The project leverages traditional machine
learning models and algorithms to analyze the datasets and identify patterns and relationships
between different variables. By applying these models, accurate predictions and recommendations
can be made regarding crop selection based on environmental factors, soil conditions, and
historical data. This enables farmers to make informed decisions, reducing the risk of low yields
and increasing productivity.
To interpret and present the analysis findings, the project utilizes data visualization
tools such as Tableau. Data visualization allows for a clear and intuitive representation of the
analysis results, making it easier for farmers to understand and utilize the information effectively.
Visualizations can help farmers visualize crop health, growth patterns, and potential risks, guiding
them in making better decisions. By providing farmers with precise insights and recommendations,
this project aims to improve the income and livelihood of small-scale and marginalized farmers. It
recognizes the challenges faced by farmers due to factors such as recent changes in farm laws and
protests. Empowering farmers with data-driven decisions can help them navigate these challenges
and enhance their overall agricultural practices. Furthermore, this project aligns with the United
Nations' Sustainable Development Goals. By enhancing crop protection, land assessment, and crop
yield prediction, the project contributes to the global efforts of achieving food security and
reducing hunger by 2030. It emphasizes the importance of sustainable agriculture practices and
data-driven decision-making in addressing the complex challenges in the agricultural sector.
In conclusion, this project aims to leverage data manipulation, machine learning, and
visualization techniques to provide farmers with valuable insights and recommendations for crop
selection and management. By empowering farmers with accurate predictions and data-driven
decisions, the project has the potential to enhance agricultural practices, improve productivity, and
contribute to the global goals of achieving food security and reducing hunger.

7. 5
LITERATURE SURVEY
Research papers in agricultural crop recommendation provide valuable insights,
methodologies, and benchmark models for your project. They offer a foundation of knowledge
about existing approaches and inspire your work. These papers detail methodologies and
algorithms that can be adapted and implemented in your project, saving time in algorithm design.
They often include benchmark datasets and performance metrics for evaluating your models.
Additionally, papers include references and citations, leading to more resources. Reading them
helps you understand the complexities of crop recommendation, from data preprocessing to model
selection. By building on prior research, you contribute to the field's knowledge. Critically evaluate
papers for relevance and adapt their insights to your context, always citing and acknowledging
other researchers' work.
A Region-Wise Weather Data-Based Crop Recommendation System Using
Different Machine Learning Algorithms
Saikat Banerjee, Dr. Abhoy Chand Mondal. The title of the paper is given as A Region-
Wise Weather Data-Based Crop Recommendation System Using Different Machine Learning
Algorithms in the journal of International Journal of INTELLIGENT SYSTEMS AND
APPLICATIONS IN ENGINEERING.
To achieve the project's goals, the implementation of various well-known algorithms has
been undertaken, including decision trees, naïve Bayes, support vector machines (SVM), logistic
regression, and random forests. These algorithms are supervised learning-based, meaning they use
labeled data to make predictions. The project has visualized the crop production results of these
algorithms for rice, potato, and wheat. The production measurements are given in thousands of
tons for rice and potato, and in tons for wheat. It is mentioned that with each technique, there is an
increase in production, but random forests and SVM perform more effectively compared to the
other models. Upon completion of the dataset training, the project team assessed the accuracy of
the implemented algorithm and compared it to the accuracy of the other algorithms. The findings
revealed that the Random Forest algorithm exhibited the highest level of precision for their dataset.
After training the dataset, the project employed the Random Forest algorithm to predict
crop harvests based on weather data attributes such as temperature, rainfall, humidity, and sun
hours. By inputting these weather values, the algorithm can provide predictions on crop yields,
allowing farmers to anticipate and plan accordingly. Overall, the project showcases the utilization
of a variety of supervised learning algorithms for crop yield prediction. The Random Forest
algorithm demonstrates promising accuracy levels for the given dataset, and it is employed to
forecast harvests based on weather conditions. This information can support farmers in making
informed decisions and mitigate risks associated with agricultural practices.

8. 6
Crop Yield Prediction Using Random Forest Algorithm for Major Cities in
Maharashtra State
Kiran Moraye is currently a final year student who is pursuing Bachelor of Engineering degree in
Information Technology from Mumbai University. The title of the paper is given as Crop Yield
Prediction Using Random Forest Algorithm for Major Cities in Maharashtra State, in the journal
of International Journal of Innovative Research in Computer Science & Technology (IJIRCST),
2021.
The goal of their project was to build a user-friendly web application which will help the farmers,
user and more policy planners to predict the crop yield based on the factor of climate change. So
they developed a web application named ’Smart Farm’. Their system was designed using python
and flask framework was used to render the results into a web page. User and Farmers of the
application can be seen in the home page and we will be able to enter the details such as the district
name, season, crop name and area of respective user’s field (in hectare). Once mandatory details
are filled on the home page user press the predicate button then the request will send toward the
server and the system gives a prediction using the model and trained under the random forest
algorithm.
The result of the prediction of the crop yield which is sent to the respective user
and the unit of the crop yield is considered in tones. The result page of the Smart Farm application,
also displays a graph of Crops that farmers can plant in his district vs. yield the respective crop
will produce. The graph suggests which crops could be planted in the Kolhapur district vs. their
respective yield. and you can see in the figure, In Kharif, season rice could be planted in the
Kolhapur district which gives more yield than Maize. This will help the user to conclude which
crop he could plant to get a better yield. In this model they trained 20 decision trees to build a
random forest. In their project, they used a 10-fold cross validation technique which gives the
accuracy of the required model, the accuracy of the model achieved 87%.

9. 7
CHAPTER 2: METHODOLOGY
2.1 DATASETS:
The dataset used in the project includes soil-specific attributes collected from Kaggle, as well
as general crop data from other online sources. The crops considered in the model include apple,
banana, chickpea, coconut, cotton, grapes, jute, maize, mango, muskmelon, papaya, pomegranate,
and watermelon.
The training dataset contains a certain number of instances for each crop. However, the
exact number of instances for each crop is not specified in the given information. The attributes
considered for the analysis are temperature, humidity, and moisture. These are essential factors
that can significantly impact crop growth and yield. By including these attributes in the analysis,
the project aims to identify relationships and patterns between these factors and the crop
production.
The dataset's soil-specific attributes, along with the temperature, humidity, and
moisture attributes, are used to train the model and make predictions on crop yields and production.
The project aims to provide farmers with valuable insights and recommendations based on these
attributes, helping them make informed decisions about crop selection and management
2.2 ALGORITHM PROPOSED:
The k-nearest neighbor (k-NN) method is a data mining technique considered to be among the
top five techniques for data mining. In this, we consider each of the characteristics in our training
set as a different dimension in some space, and take the value an observation has for this
characteristic to be its coordinate in that dimension, so getting a set of points in space. We can
then consider the similarity of two points to be the distance between them in this space under
some appropriate metric.
The way in which the algorithm decides which of the points from the training set are
similar enough to be considered when choosing the class to predict for a new observation is to
pick the k closest data points to the new observation, and to take the most common class among
these. This is why it is called the k Nearest Neighbors algorithm. The implementation of
algorithm can be noted as below:
1. Load the data
2. Initialize K to your chosen number of neighbors
3. For each example in the data
Calculate the distance between the query example and the current example from the data. Add
the distance and the index of to an ordered collection.
4. Sort the ordered collection of distances and indices from smallest to largest (in ascending
order) by the distances
5. Pick the first K entries from the sorted collection
6. Get the labels of the selected K entries

10. 8
7. If regression, return the mean of the K labels
8. If classification, return the mode of the K labels
Here we consider parameters like soil type, PH, Nitrogen, Phosphorous, Potassium etc. We have
assigned N, P, K, soil type as input parameters although other parameters may also be
considered. The crop yield which is an unknown value can be predicted using the values of the
nearest known neighbors.

11. 9
2.3 BLOCK DIAGRAM:
The block diagram serves as an overall view of the Smart Farming: Data-Driven Crop
Recommendation System. It illustrates the different components and their connectivity within the
system.
The components used in this project include sensors for measuring environmental factors
such as temperature, humidity, soil moisture, and sunlight. These sensors provide real-time data
that is crucial for making accurate crop recommendations. The final output of the system is a set
of crop recommendations that farmers can use to make informed decisions about their farming
practices. These recommendations aim to optimize crop yield, minimize resource wastage, and
improve overall productivity.
DATASET COLLECTION
Training Data
FEATURE EXTRACTION
RECOMMENDATION SYSTEM
( BASED ON RULES)
ENSEMBLE MODEL
K-NEAREST NEIGHBOUR
CROP TO BE YIELD
RULES
INDUCTION
T
E
S
T
I
N
G
D
A

12. 10
CHAPTER 3: PROCESS & REQUIREMENTS
3.1 SOFTWARE REQUIREMENTS:
3.1.1 Jupyter Notebook
Jupyter Notebook is an open-source web application known for its versatile capabilities. It allows
users to create and share documents combining live code, equations, visualizations, and text. With
Jupyter, tasks such as data cleaning, numerical simulation, statistical modeling, data visualization,
and machine learning become easily accessible. One of the key reasons for its popularity is its
support for over 40 different programming languages, including Python.
The Python programming language has become particularly favored in the Jupyter Notebook
environment due to its simplicity, readability, and the vast range of libraries and frameworks
available, making it a powerful tool for data analysis and scientific computing.
3.2 COMPONENTS DESCRIPTION:
3.2.1 Arduino Mega 2560:
The Arduino Mega 2560 is a feature-rich microcontroller board that is built around the
ATmega2560 microcontroller. With its 54 digital input/output pins, including 15 with PWM
capabilities, and 16 analog inputs, it offers users a vast array of options for connecting and
controlling external components. The board also includes four hardware UARTs, a 16 MHz crystal
oscillator, a USB connection, a power jack, an ICSP header, and a reset button, providing all the
essentials for seamless microcontroller integration. Additionally, the Mega 2560 board is
compatible with shields designed for the Arduino Uno, Duemilanove, and Diecimila boards,
allowing for easy expansion and integration with existing hardware. The Arduino Mega 2560 is a
highly versatile and reliable microcontroller board that provides extensive features and
compatibility options for various projects. Its abundant I/O capabilities, multiple UARTs, and
support for shields ensure seamless connectivity and expansion possibilities. Whether you're a
beginner or an experienced developer, the Mega 2560 is a popular choice due to its flexibility and
ease of use. Simply connect it to a computer via a USB cable or power it using an AC-to-DC
adapter or battery, and you're ready to start working on your project.
The Mega 2560 is an update to the Arduino Mega, which it replaces.

13. 11
• Microcontroller: ATmega2560
• Digital I/O Pins: 54 (of which 15 provide PWM output)
• Analog Input Pins: 16
• Flash Memory: 256 KB of which 8 KB used by bootloader
• SRAM: 8 KB (ATmega2560)
• EEPROM: 4 KB (ATmega2560)
• Clock Speed: 16 MHz
3.2.2 Soil NPK sensor:
The soil NPK sensor is purpose-built for the accurate detection and measurement of nitrogen (N),
phosphorus (P), and potassium (K) content in soil, playing a crucial role in assessing soil fertility.
Its stainless steel probe is designed to withstand long-term burial and demonstrates exceptional
resistance against electrolysis, salt, and alkali corrosion. This ensures the durability and reliability
of the sensor, even in challenging environmental conditions. Additionally, the sensor is fully
encased in a waterproof shell through vacuum potting, providing complete protection against
moisture and other potential factors that may affect its performance. Overall, the soil NPK sensor
offers a robust and dependable solution for effectively analyzing soil fertility. By leveraging its
durable construction, including the stainless steel probe and waterproof casing, the sensor enables
accurate and long-lasting measurements of NPK content in soil. With its ability to withstand
diverse environments and deliver reliable insights, the soil NPK sensor proves invaluable for

14. 12
farmers, researchers, and environmentalists in optimizing agricultural practices and ensuring
sustainable land management.
3.2.3 Soil Moisture Sensor:
Soil moisture sensors are valuable tools that measure the volumetric water content in soil
indirectly, avoiding the need for time-consuming and intrusive gravimetric measurements. Instead,
these sensors rely on properties such as electrical resistance, dielectric constant, or neutron
interaction to serve as proxies for moisture content. By analyzing changes in electrical
conductivity, dielectric constant, or neutron interaction, soil moisture sensors provide an estimate
of the water content in the soil. This indirect measurement approach offers a convenient and non-
destructive method for monitoring and evaluating soil moisture levels, enabling efficient irrigation
practices and effective water management in various agricultural and environmental applications.

15. 13
3.2.4 DHT11 Sensor:
The DHT11 is a popular and widely used sensor specially designed for measuring temperature and
humidity. It incorporates a dedicated NTC (Negative Temperature Coefficient) thermistor to
accurately detect temperature changes. Additionally, it features an 8-bit microcontroller that
processes the collected data and outputs the temperature and humidity values as serial data. This
compact and integrated design allows for easy integration and communication with other devices
or microcontrollers. With the DHT11 sensor, users can reliably and conveniently monitor
temperature and humidity in various applications such as weather monitoring, environmental
control systems, and smart agriculture. Its simplicity, affordability, and reliable performance have
made the DHT11 a preferred choice for hobbyists, students, and professionals alike. Whether it is
for temperature regulation or maintaining optimal environmental conditions, the DHT11 proves to
be a valuable and practical sensor solution.
3.2.5 LCD (Liquid Crystal Display):
LCD (Liquid Crystal Display) is a type of flat panel display which uses liquid crystals in its
primary form of operation. LEDs have a large and varying set of use cases for consumers and
businesses, as they can be commonly found in smartphones, televisions, computer monitors and
instrument panels.

16. 14
CHAPTER 4: RESULT, CONCLUSION AND FUTURE SCOPE
4.1 RESULT:
In the course of our crop recommendation project, we meticulously trained and evaluated a
machine learning model, specifically the K-nearest neighbors (KNN) algorithm. This model served
as the cornerstone of our recommendation system, utilizing soil and environmental data to provide
accurate crop suggestions. To assess the model's accuracy, comprehensive evaluations were
carried out, ensuring its efficacy in matching suitable crops to the given soil and environmental
conditions. To render our results comprehensible, we harnessed visualization techniques such as
confusion matrices, bar plots depicting feature importance, and classification reports. These
visualizations acted as powerful tools, offering an intuitive representation of our model's
performance. The confusion matrices provided a clear snapshot of the model's predictive accuracy,
while bar plots vividly illustrated the relative importance of different features. Additionally,
classification reports succinctly summarized the precision, recall, and F1-score metrics, enhancing
our understanding of the model's overall performance. In essence, our results not only validated
the effectiveness of our KNN-based recommendation system but also provided invaluable insights
into the fundamental factors steering crop selection.
Model Information Visualization
In this part of the chapter, the visualizations with respect to the model will be discussed.

17. 15
Correlation
The correlation between attributes allows us to determine how strongly or weakly they are
related to one another.
Accuracy

18. 16
4.2 CONCLUSION
This project will assist farmers in sowing the appropriate seeds for their soil and location.
Because this is a deliberate effort, the chances of the outcome benefiting the farmer are extremely
high. The government's engagement and use of the project's concept on a much wider scale has
the potential to revolutionize the landscape of agriculture in this country. This innovation can
produce a wide assortment of yields. Indian ranchers might enjoy the benefit of precisely
foreseeing yields in various parts of India.
4.3 FUTURE SCOPE
In the future, we will try to collect different data for training and testing. The work that will be
done in the future will centre on recommending the succession of products that should be produced
depending on the circumstances of the land and the weather, as well as regularly updating the
databases in order to make accurate projections. In addition to that, the latest ML algorithms will
be introduced to the model to increase its accuracy. The most advantageous aspect of applying
ML in agriculture is that it will not cause human farmers' employment to be eliminated;
rather enhance the processes that farmers currently use.

19. 17
CHAPTER 5: REFERENCES
5.1 REFERENCES
• Banerjee, S. ., & Mondal, A. C. . (2023). A Region-Wise Weather Data-Based Crop
Recommendation System Using Different Machine Learning Algorithms. International
Journal of Intelligent Systems and Applications in Engineering, 11(3), 283–297.
• Kiran Moraye, Aruna Pavate, Suyog Nikam and Smit Thakkar(2021), “Crop Yield
Prediction Using Random Forest Algorithm for Major Cities in Maharashtra
State”, International Journal of Innovative Research in Computer Science &
Technology (IJIRCST), ISSN: 2347-5552, Volume-9, Issue-2
• S., Rajeswari & Kannan, Suthendran & Rajakumar, K.. (2018). A smart agricultural
model by integrating IoT, mobile and cloud-based big data analytics. International
Journal of Pure and Applied Mathematics. 118. 365-369.
• Elijah, Olakunle & Abd Rahman, Tharek & Orikumhi, Igbafe & Leow, Chee Yen &
Hindia, Mohammad. (2018). An Overview of Internet of Things (IoT) and Data Analytics
in Agriculture: Benefits and Challenges. IEEE Internet of Things Journal. PP. 1-1.
10.1109/JIOT.2018.2844296.