It is good for crops

1. CONTENTS
1. Introduction
2. Literature Survey
3. Existing system and drawbacks
4. Problem identification / Statement
5. Objectives
6. Methodology
8. System Requirements
9. References
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2. INTRODUCTION:
Agriculture is the backbone of our Indian economy, which plays an important role in increasing our country's economic
development. The main focus of this app is on the agricultural society because the farmers have the highest contribution
to our country's GDP.
This application provides this information to the farmer in real time and introduce farmers to the most to suitable
crops to be sown along with fertilizer recommendation and plant disease detection, thereby helping them to achieve
greater productivity with reduced loss.
• Renting Tools :At present, farmers need to travel to a place to borrow all the essential needs, which is a tiresome and not
a cost effective work. So a smart digital farming is listed as the highest ranking technology opportunity in the latest
Global Opportunity report in terms of its expected positive impact on society
• Climate: Small changes in temperature and rainfall have significant effects on the quality of crops. This application helps
in detecting suitable weather for particular crop.
• Expert Assistance : This includes contacts and feedbacks
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3. LITERATURE SURVEY
SL
NO
TITLE YEAR DESCRIPTION ADVANTAGE DISADVANTANGE
1. “Plant Disease
Detection and
Classification by
Deep Learning-A
Review”
2021 The application of deep
learning in plant disease
recognition can make plant
disease feature extraction
more objective, and improve
the research efficiency and
technology transformation
speed.
The advantage of this method
was that multiple diseases on
the same leaf could be detected
and the data can be augmented
by cutting up the leaf image into
multiple sub-images.
The system also has some
inadequacies. The DL
frameworks proposed
have good detection
effects but the effects are
not good on the datasets,
that is the model has poor
robustness.
2. “Recommendation of
Crop ,Fertilizers and
Crop Disease
Detection System”
2021 The system is called Agro
Consultant system consisting
of three models namely : crop
recommendation, Fertilizers
recommendation and crop
disease detection.
The system can be easily used
by farmers all over India. This
system would assist the farmers
in making an informed decision
about which crop to grow,
efficient use of fertilizers and
prediction on the type of crop
disease based upon the textual
similarity of leaves.
A small change in the data
can cause a large change
in the structure causing
instability. Decision tree
takes far more time to
train the model.
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4. 4
3. “Smart Agriculture
Using Deep Learning
Technologies: A
Survey”
2022 The application of deep
learning in plant disease
recognition can make plant
disease feature extraction
more objective, and
improve the research
efficiency and technology
transformation speed.
The benefit is that the same
neural network-based
approach can be applied to
several different applications
and data types. Deep Learning
also offers good
generalization performance.
One of the notable
drawbacks is that it
requires many datasets
to give better
performance, even if
data augmentation
techniques are used.
4. “Crop Guidance and
Farmer’s Friend- Smart
Farming using Machine
Learning”
2022 The main aim is to assist
the farmers with expanding
their yield. The proposed
system is designed to track
weather conditions at a
specific location, such as
temperature, humidity and
changes in the environment.
This application diminishes
the time and efforts of farmers
and assists them with getting
day-to-day market cost of
various harvests, fertilizers
and vegetables without
visiting the market.
The system has an
accuracy of only about
79.09% for Naïve
Bayes Algorithm for
crop prediction and
accuracy of 71.2% for
disease Prediction using
CNN.

5. 5
5. “Machine Learning in
Precision Agriculture: A
Survey on Trends ,
Applications and
Evaluations Over Two
Decades ”
2022 This paper identifies and
describes some of the key
data issues and study of the
impact of these data issues
on various machine
learning approaches within
the context of agriculture.
Monitoring provides
automatic data collection of
various parameters including
soil data such as moisture and
chemistry, crop data including
leaf area and plant height, and
weather data including rainfall
and humidity.
There is vast variation
in the metrics used to
assess classification
accuracy. The
implementation of IoT
in agricultural domain
has several problems to
be considered and
solved .Three major
types of limitations, are
at the application,
network and device
levels.

6. 5
EXISTING SYSTEM AND DRAWBACKS
.
• Has got the highest accuracy of 79.09% for Naïve Bayes Algorithm for crop
prediction and 71.2% accuracy for disease prediction using CNN.
• The existing system requires many separate applications to carry out crop prediction,
plant disease detection and so on.
• Existing system has limited features such as only plant disease prediction. Although it
addresses crop diseases in large volume, it doesn't address all crop diseases.
• Farmers lack expert guidance in the existing system.
• Unavailibility of climate/weather changes throughout the crop cultivatation to
perform necessary farming activities.

7. PROBLEM STATEMENT
To design and develop a system using deep learning to
support farmers in early identification of plant diseases and
to recommend suitable crops & fertilizers for cultivation.
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8. OBJECTIVES
• The main objective of this system is to provide
• crop prediction,
• fertilizer recommendation,
• plant disease detection,
• information about climate,
• current news and expert assistance.
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9. METHODOLOGY
1. Crop Recommendation:
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10. 2.Plant Disease Detection and Solution
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11. 3.Climate/Weather Forecast
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12. Expert Assistance :
Contact details of the experts will be provided where the registered farmer
can call/mail them and also leave a feedback.
Renting Tools :
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13. Usecase Diagram
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14. Sequence Diagram
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15. Hardware and Software Requirements
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• Hardware requirements:
 Display screen / Desktop
 Keyboard
 Web Cam
 Touchpad / Input device
 2 GB/4 GB RAM
• Software Requirements:
 Python IDE 3.7
 Pycharm

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23. REFERENCES
1. Tanvi Daware, Pratiksha Ramteke, Uzma Shaikh, and Smita Bharne, Crop Guidance and
Farmer’s Friend – Smart Farming using Machine Learning, ITM Web of Conferences 44,
03021 (2022) ICACC-2022
2. LILI LI, Shujuan Zhang, and Bin Wang, Plant disease detection and classification by
deep learning –A review,IEEE-2021
3. Sarah Condram, Michael Bewong, MD Zahidul islam, Lancelot Maphosa, and Lihong
Zheng, Machine learning in precision agriculture: A survey on trends , applications and
evaluations over two decades, IEEE-2022.
4. V. R. V. S. S. T. Sooraj Krishna, Sreevijay, Farmer friend, IEEE, 10, pp. 9–19 (2021)
5. G. A. Anirudha Vachaspati Vempati, Agricultural problems and technology-based
sustainable solutions, IEEE, 5, pp. 6–13 (2020)
6. S. N. Uke1 , Prafulla Mahajan2 , Swapnali Chougule3 , Vaishnavi Palekar4 , Kaivalya
Kashikar, IJRASET,-2021
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24. Thank You