The tech will help farmers alleviate current pressure due to food scarcity and reduce the repercussions on the environment.

1. Top 4 applications of AI, ML in agriculture
The tech will help farmers alleviate current pressure due to
food scarcity and reduce the repercussions on the
environment.
With over 1.3 billion people employed in the agriculture sector
worldwide, this $5 trillion industry is the 2nd largest employment
generator in the world accounting for more than 28% of the global
employment. Being around 12, 000 years old, it is the primary
source of living for almost 58% of India’s population.
It is estimated that the world’s population would reach 9.7 billion
by 2050 and the supply side could face constraints due to an
increase in demand for food, requiring at least 70% increase in
production. And the applications of Artificial Intelligence and ML in
the agriculture industry is estimated to improve the industry by at
least 7 to 9% by adding a value of another $500 billion by 2030 to
the GDP. There are several applications of this advanced
connectivity in the agriculture industry.

2. Field conditions management
Determining the soil conditions and its complex processes are done
by the use of these connectivity technologies. The right conditions
for the crop to grow, identifying the nutrient deficiencies in the soil,
and enabling image recognition technology to improve the harvest
quality are done by the use of AI applications in fields.
Machine learning algorithms analyse the soil by examining the
soil moisture, temperature, evaporation processes, to
understand the mechanisms of the entire ecosystems and find ways
to restore the soil and produce healthy crops.
Livestock management
As Artificial intelligence and machine learning have become easily
available now, the use of applications pertaining to this technology
has benefited the farmers greatly to manage their livestock
efficiently. AI technology has revolutionized farming by using
applications like robots, drones and smart monitoring
systems. Apart from this, technologies created to monitor the
health of farm animals, to detect injuries and illness saves the time
and effort of the farmer.
Machine learning benefits the farmers with its smart technology
such as weight prediction systems that enable the farmers to
monitor the weight and adjust the diet of the animals. Besides this,
applications provide the farmers with facts and analytics through a
medium of chatbots to answer their queries and yield them
better results of success.

3. Crop management
The applications of Artificial Intelligence and Machine learning are
both utmost important in the field of agriculture. The applications
of machine learning help the framers to do their core work
more easily and stress free. The ML powered support vector
machines help the farmers to supervise over the farms and are
largely used in the rice fields to yield the quantity and making
process. Apart from this, machine learning also has many
applications to support the farmers to detect any particular
disease and take action by spraying the pesticide on the basis
of the location, hour and area of affected crops.
Artificial Intelligence has also played a prominent role in the
field of agriculture. The use of drones that have embedded cameras
and sensors enable the information of a particular plant in detail
which helps to monitor the crops efficiently. Apart from this, farmers
use IoT sensors that help them to protect the crops from pests
and prevent damage to the crop.
Species management
Selecting the right species is not an easy task as it involves
searching for the right species, regulating the nutrients used,
adapting to climate change, checking for ability to stand
diseases and a lot more. Here machine learning applications
analyse the years of field data to help farmers in predicting the right
genes according to the changing weather conditions. It also aids in
quick and way more accurate results to classify the species by
embedding morphology in its systems.

4. Whereas AI aids in protecting the species by implementing
cloud-based technology to take images using traps and send the
data faster saving resources and time. This technology also
predicts the seasonal movement of insects and migration of
birds, helping in keeping track of species, preventing loss, and
increasing farming efficiency.
In the recent years , the agriculture industry has begun to use AI
and ML to increase productivity and it is expected to contribute
another $2 to $3 trillion to the global GDP in the next few years
while helping farmers to alleviate the current pressure due to the
issue of food scarcity and also reducing the repercussion on the
environment.