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1. E-CROP MONITORING
SYSTEM
Integerated with organic farming
PRIYANKHA J(BE-3rd)
KAMALESH V(BE-3rd)
PROPOSED BY

2. INTRODUCTION
Smart agriculture is being implemented to minimise farmers'
time consumption and make it more cost-effective. The
autoencoder in smart farming uses a homogeneous set of
features to
detect anomalies within agricultural fields. Anomaly detection
with deep learning establishes
smart farming, where it predicts the vast amount of data.
Machine learning extracts the massive
amount of context information. As the population increases,
food becomes scarce.
A high level of agricultural production is sustained with a
regression algorithm that improves diversification.
To determine the complexity of the behavior, the pattern type
must not exhibit the expected set
of behaviours. To detect the aberrant set of behaviours,
anomaly detection is used. Monitor all
the agricultural data and livestock regions using different
collection procedures.

3. ABSTRCT
This concept has not yet been created or relies on an exist
system. In the present system,there is no existing control
over crop cultivation and harvest that is done by the
customers in direct. Thisapplication completely deals with
precised and regulated records of customer’s needs in field of
cropcultivation. In general we have many products that
predicts the perfect harvest materials and harvest
time duration, but this works in a way ahead by gathering all the
real time records from users and thefurther cultivation is done.
Major role of this idea depends on the balance of profit and
harvest data of farmers. Thereby reducing frequent peaks in
crop rates due to heavy demand.

4. DESIGN ANALYSIS
Digital agricultural application which predicts the requirement of the essential raw
materials that are been cultivated by the farming society.it consists of a general login
page where user can login through their login credentials and post their necessary or
daily requirement(AGRO based raw materials) the application contains the list
of all kind of cultivation crops that the user can select the required raw materials and
post their need the database is send to the farming society through the government
portal responsible for farming
=>login credentials(user or cultivator ,open to all).
=>create user id.
=>home page.
=>updation current crop availability (farming society) .
=>crop requirement (list of cultivation crops).
=>server handling database(state govt of tamilnadu).
=>cost of raw materials (farmers fixing price).
=>information storage in back-end through database.
=>updation of requirements by users.
=>estimated productivity rate for next harvest
Back to Agenda Page

5. Machine Learning algorithms such as SVM and decision tree
classifier
AI can provide farmers with real-time insights from their fields,
allowing them to identify areas that need irrigation, fertilization,
or pesticide treatment. The result is reduced use of pesticides,
better harvest quality, higher profits, and significant cost
savings.
DEEP LEARNING AND ML ALGORITHM
Deep learning algorithms are being used in smart agriculture to
monitor related parameters and to observe it globally.
ML algorithm that is a complementary tool which is used in our
design for crop production rate and yeild prediction.
ALGORITHM AND TECHNIQUES :

6. VERMICULTURE SCHEDULE
The fertilizers based with the things locally available or the app
helps in providing information with the things at low cost Thus
helping those farmers who are in need of it . This provides
income.
Green manuring -- composting or growing in the soil
Amrutha paani -- cowdung, cow uring and jaggery. 24 hours tonic
is ready and successfully used by farmers.
Herbal pest repellent -- cow urine based
Panchagavya -- healthy tonic.
Farmer's cytosum -- coconut milk and buttermilk fermented and
diluted with water
Manure tea -- cowdung, leaves, jaggery, 200 litre drum of water --
1 week to prepare.
Gunabasalam -- waste of any dead animal and mix with jaggery
and keep for 25 days
10 eggs in a plastic container and lemon juice is added until eggs
are completely submerged. add jaggery and 20 days you have
egg-lemon extract.

7. This system idea is to mainly improvise the demand and harvest of
agricultural crops.
This application Basically works as a government organised
application where customersand farmers can directly log in
through the app and raise for the crop which they require.
Basic idea is that according to the sectors that is been divided in
TAMILNADU, the appropriate crop selection will be regionally
selected and the particular crop will be sowed in those interiors.
This separation of cultivation is based on the soil fertility and
harvest periods. After theharvest of the demanded crops updated
in the application , further import of the cultivated crops may be
done within TAMILNADU .
Automatically the excessive crops after seperation will be exported
to other neighborhoods.
NOVELTY

8. PROJECT SUMMARY
The project's goal is to connect all the farmers and provide
them technological assistance using AI. It will serve the
farmers in the best possible ways to increase the standards
and their income. An AI based integrated system for
Organic farming will be developed. The features offered by
the system will include Crop Prediction and Protection
based on environmental conditions considering the biotic
and abiotic factors, increasing the yield through
Vermiculture and controlled use of organic fertilizers
An AI based integrated system for Organic farming will be
developed. The features offered by the system will include
Crop Prediction and Protection.

9. SOFTWARE FLOW CHART

10. INTEGERATED AI SYSTEM

11. Outcomes
Technological solution to farmers using AI to
significantly improve the yield.
Farmer friendly system that autonomously
monitors and controls all aspects associated
with the growth of crops.
Crop Prediction based on environmental
conditions when to plant and when to harvest.
Continuous Crop Growth Monitoring.
Continuous monitoring of biotic and abiotic
factors influencing the growth of the crop.
Image processing to identify pests and
deformities in the crops.
Promote Vermiculture for controlled use of
fertilizers and improve the yield.
Anomaly detection to compute the difference
between the predicted results and the attained
results.
OUTCOME

12. RFID TRANSMITTER & RECEIVER (RS 10,000) CROP MONITORING SENSOR (RS 5,000)
WEATHER SENSING UNIT (RS 15,000)
SPECTRAL IMAGING CAMERA & SURVEILLANCE
SYSTEM ( RS 10 L)
SERVICE PROVIDER (RS 10,000) IOT SERVERS (RS 1.5 L)
TOTAL COST APPROX (RS 30 L)
FINANCIAL ANALYSIS

13. Thank
Thank
Thank
you!
you!
you!