Application of data science in agriculture.

1. APPLICATION OF DATA SCIENCE IN
AGRICULTURE

2. Introduction
Agriculture is the most prominent sector in the Indian
economy, employing almost half the population. It
involves producing, processing, and distributing food,
fibre, and other products. The continuous increase in
population puts added pressure on the agriculture
industry. Thus needs to adopt new technologies to
keep up with the demands, and data science is one of
them. Data science has the potential to revolutionize
the agriculture industry, making it more efficient and
sustainable by providing farmers with tools to improve
their operations, reduce waste, increase yields, and
address the challenges faced by the industry.

3. The agriculture industry faces several challenges, including
climate change, water scarcity, soil degradation, pests and
diseases, and food security which affects its productivity and
profitability.
Food security is a global challenge that affects millions of
people. It involves ensuring that everyone has access to
sufficient, safe, and nutritious food.
Pests and diseases are a significant threat to agriculture,
affecting crop yield and quality. The use of pesticides and
chemicals has its own set of environmental and health
concerns.
CHALLENGES FACED BY AGRICULTURE SECTOR

4. Climate change has resulted in extreme weather conditions,
such as droughts, floods, and heat waves, which affect crop
production and livestock.
Soil degradation is another problem that affects the
productivity of agriculture. Soil erosion, nutrient depletion,
and soil compaction are some of the issues that need to be
addressed.
Water is becoming more and more in short supply worldwide,
and more than one-third of the world's population would face
total water shortage. Making it necessary to use water in a very
efficient manner.

5. DATA SCIENCE ROLE IN SOLVING THESE
Data science can be used to create precision agriculture
systems that use sensors, drones, and other technologies to
monitor crops and soil. This helps farmers optimize their
inputs, reduce waste, and increase yields. Precision
agriculture involves collecting and analyzing data on
weather patterns, soil moisture, crop growth, and other
variables to make informed decisions about planting,
fertilizing, watering, and harvesting crops.
One project involves giving IoT sensors to Taiwanese
farmers of rice production so they can collect information
that is necessary about their crops. It will help farmers to
optimize their production cycles, even if climatic changes
make it challenging.

6. Data science can be used to analyze soil samples and provide
farmers with information about nutrient levels, pH levels, and other
variables. This helps farmers make informed decisions about which
crops to plant and which fertilizers to use.
Farmers Edge, a Canadian company, takes daily satellite images of
farms and combines them with other relevant data.
Several countries like Ireland also depend on satellite-based soil and
crop monitoring to inspect areas more quickly than traditional
methods allow.
Due to the complexity of finding the optimal fertilization range the
majority of farmers still rely on trial and error, guesswork and
estimation, thus resulting in reduced crop yield potential, and
increased environmental pollution. Data science professionals are
now able to advise farmers about the right quantity of fertilizers.

7. Data science can be used to develop algorithms that can detect
diseases and pests early. Early detection can help farmers take
action before the problem becomes widespread, reducing the
need for chemicals and pesticides. Agricultural pests can
quickly cut into a farmer’s profits. But, misusing pesticides can
have adverse effects on people, plants and other living things.
While some insects can be incredibly beneficial to farmers and
crops, others can be toxic and spread diseases. Disease
detection can be done by taking images of the field using
drones and processing them to detect areas within this field
that are infected.
Data science can be used to track food from farm to table,
ensuring that it is safe, nutritious, and of high quality. By
analyzing data on transportation, storage, and other variables
and helping ensure that food is delivered to consumers in a
timely and efficient manner.

8. DATOS project applied artificial intelligence,
machine learning, and other data science techniques
to remotely sense data. Apart from that, it used
systems to produce geospatial outputs that can be
used for disasters, agriculture and other purposes.
The DATOS Project has developed a way to map out
crops by using satellite images and by extracting the
temporal signature of crops determined through radar
satellite images.
Moreover, DATOS produces flood situation maps by
retrieving satellite images and letting AI identify
flooded areas from these images and sends these
mapped-out areas to the respective DOST regional
offices in the event of severe weather disruptions.

9. With the movement of the world towards digital agriculture, a lot of investment has
been poured into it. Numerous research and development are going on to maximize
the efficiency of farms.
Incorporating new technology will escalate the yields of both small- and large-scale
farms. New trends in farming, including data science and analytics, will revolutionize
the agriculture industry, producing higher-quality foods in a larger quantity
sustainably so that the global target of increasing food production can be met.

10. Thank You
Archana p
2237027
2MAECO