Machine Learning and Agriculture

5 min readDec 8, 2020


The world’s population is on track to reach 9.7 billion by 2050,
requiring a corresponding 70% increase in calories available for
consumption, even as the cost of the inputs needed to generate those
calories are rising. By 2030, the water supply will fall 40%short
of meeting global water needs, and rising energy, labour, and nutrient
costs are already pressuring profit margins. About one-quarter of arable
the land is degraded and needs significant restoration before it can again
sustain crops at scale. And then there are increasing environmental
pressures, such as climate change and the economic impact of
catastrophic weather events, and social pressures, including the push
for more ethical and sustainable farm practices, such as higher
standards for farm-animal welfare and reduced use of chemicals and

Digital tools are needed to deliver the next productivity leap. Some
already exist to help farmers more efficiently and sustainably use
resources, while more advanced ones are in development. These new
technologies can upgrade decision making, allowing better risk and
variability management to optimise yields and improve economics. A
change in the global ageing demographic has triggered the adoption of
automation in farming practices. Automation and control systems
manufacturers have witnessed a definite surge in their sales due to this
profound change in the farming industry.
To address the mentioned forces poised to further roil the industry,
agriculture must embrace a digital transformation enabled by Machine


The agriculture industry has radically transformed over the past 50
years. Advances in machinery have expanded the scale, speed, and
productivity of farm equipment, leading to more efficient cultivation of
more land. Crop yield relies strongly on how effectively the basic land
requirements can be utilised; land here refers to topography, soil type,
soil nutrients, water content, sunlight, and all such factors related to
crop growth on farmable areas.

Machine Learning is the scientific field that gives machines the ability
to learn without being strictly programmed. It has emerged together with
big data technologies and high-performance computing to create new
opportunities to unravel, quantify, and understand data intensive
processes in agricultural operational environments.

Machine learning is everywhere throughout the whole growing and
harvesting cycle.
It begins with a seed being planted in the soil — from
the soil preparations, seeds breeding and water feed measurement — and it
ends when robots pick up the harvest determining the ripeness with the help of computer vision.

Machine learning is among the trending technologies; hence, there exist
several technologies and systems that run on a machine learning



  1. Soil management

For specialists involved in agriculture, the soil is a heterogeneous natural
resource, with complex processes and vague mechanisms. Its temperature
alone can give insights into the climate change effects on the regional
yield. Machine learning algorithms study evaporation processes, soil
moisture and temperature to understand the dynamics of ecosystems and
the impingement in agriculture.

2. Water Management

Water management in agriculture impacts hydrological, climatological,
and agronomical balance. So far, the most developed ML-based
applications are connected with an estimation of daily, weekly, or monthly
evapotranspiration allowing for more effective use of irrigation
systems and prediction of daily dew point temperature, which helps
identify expected weather phenomena and estimate evapotranspiration and


  1. Yield Prediction

Yield prediction is one of the most important and popular topics in
precision agriculture as it defines yield mapping and estimation,
matching of crop supply with demand, and crop management. State-of
the-art approaches have gone far beyond simple prediction based on the
historical data, but incorporate computer vision technologies to provide
data on the go and comprehensive multidimensional analysis of crops,
weather, and economic conditions to make the most of the yield for
farmers and the population.

2. Crop Quality

The accurate detection and classification of crop quality
characteristics can increase product price and reduce waste. In
comparison with the human experts, machines can make use of seemingly
meaningless data and interconnections to reveal new qualities playing
role in the overall quality of the crops and to detect them.

3. Disease Detection

Both in open-air and greenhouse conditions, the most widely used
practice in pest and disease control is to uniformly spray pesticides
over the cropping area. To be effective, this approach requires
significant amounts of pesticides which results in a high financial and
significant environmental cost. ML is used as a part of the general
precision agriculture management, where agrochemicals input is targeted
in terms of time, place and affected plants.

4. Weed Detection

Apart from diseases, weeds are the most important threats to crop
production. The biggest problem in weeds fighting is that they are
difficult to detect and discriminate from crops. Computer vision and ML
algorithms can improve detection and discrimination of weeds at low cost
and with no environmental issues and side effects. In future, these
technologies will drive robots that will destroy weeds, minimising the
need for herbicides.


In recent times, several machine learning systems in agriculture have
been tested and created. Research of several machine learning
algorithms’ effectiveness in agriculture and other application domains
has also been conducted and this is because machine learning is a very
an effective tool for efficient use of resources, prediction, and
management, which is needed in agriculture.

Over the past five years, agricultural robots have also been
incorporated into farming operations as they treat soil and crops
selectively as per their requirements and reduce the need for manual
labour. UAV/drones generated the highest revenue amongst all agricultural
robots utilised in smart farming. The majority of robot deployment was
done for crop management.

The Asia-Pacific region is projected to display the fastest market
growth from 2017 to 2022. The region presents immense scope for
market development, owing to the increasing urban population size,
growing market penetration of internet in farm management, and
favourable government investments. Moreover, the presence of
economically advancing countries such as India and China are expected to
make the region a primary part of the growth of precision agriculture in
the upcoming years.

Driverless tractors tilling acres of crops produce growing in massive
climate-controlled warehouses, and seeds genetically altered to require
less water are among the high-tech innovations changing, or about to
change, agriculture. These technologies are making farms smarter, more
productive, and increasingly efficient.

More precise GPS controls paired with computer vision and sensors could
advance the deployment of smart and autonomous farm machinery. Farmers
could operate a variety of equipment on their field simultaneously and
without human intervention, freeing up time and other resources.
Autonomous machines are also more efficient and precise at working afield than human-operated ones, which could generate fuel savings and
higher yields.

Even then, for an industry that lags behind others in adopting
technology, the challenges go beyond investment money flowing into
Agritech. Smarter farms also require smarter workers who can operate the
new technology. And business and government regulations, trade and tax
policies, and even basic technology infrastructure must support these
innovative farming techniques.

There is also something less tangible that no policy can change.
Probably the biggest challenge is the fact that people like doing things
the way they used to do them in the past. This industry was not a
leading user of information technology, and as a result of that, you
need to change the mind-set first.




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