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Machine Learning in Agriculture: Applications and Techniques
Smart Farming makes
agriculture more efficient and effective with the help of high-precision
algorithms. The mechanism that drives it is Machine Learning — 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 preparation, seeds breeding
and water feed measurement — and it ends when robots pick up the harvest
determining the ripeness with the help of computer vision.
Let’s discover
how agriculture can benefit from Machine Learning at every stage:
Species management
Species Breeding
Our favorite,
this application is so logical and yet so unexpected, because mostly you read
about harvest prediction or ambient conditions management at later stages.
Species selection
is a tedious process of searching for specific genes that determine the
effectiveness of water and nutrients use, adaptation to climate change, disease
resistance, as well as nutrients content or a better taste. Machine learning,
in particular, deep learning algorithms, take decades of field data to analyze
crops performance in various climates and new characteristics developed in the
process. Based on this data they can build a probability model that would
predict which genes will most likely contribute a beneficial trait to a plant.
Species Recognition
While the
traditional human approach for plant classification would be to compare color
and shape of leaves, machine learning can provide more accurate and faster
results analyzing the leaf vein morphology which carries more information about
the leaf properties.
Field conditions
management
Soil management
For specialists
involved in agriculture, 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.
Water Management
Water management
in agriculture impacts hydrological, climatological, and agronomical balance.
So far, the most developed ML-based applications are connected with estimation
of daily, weekly, or monthly evapotranspiration allowing for a more effective
use of irrigation systems and prediction of daily dew point temperature, which helps
identify expected weather phenomena and estimate evapotranspiration and
evaporation.
Crop management
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 population.
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.
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 agro-chemicals
input is targeted in terms of time, place and affected plants.
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, minimizing the need for herbicides.
Livestock management
Livestock Production
Similar to crop
management, machine learning provides accurate prediction and estimation of
farming parameters to optimize the economic efficiency of livestock production
systems, such as cattle and eggs production. For example, weight predicting
systems can estimate the future weights 150 days prior to the slaughter day,
allowing farmers to modify diets and conditions respectively.
Animal Welfare
In present-day
setting, the livestock is increasingly treated not just as food containers, but
as animals who can be unhappy and exhausted of their life at a farm. Animals
behavior classifiers can connect their chewing signals to the need in diet
changes and by their movement patterns, including standing, moving, feeding,
and drinking, they can tell the amount of stress the animal is exposed to and
predict its susceptibility to diseases, weight gain and production.
Farmer’s Little Helper
This is an application
that can be called a bonus: imagine a farmer sitting late at night and trying
to figure out the next steps in management of his crops. Whether he could sell
more now to a local producer or head to a regional fair? He needs someone to
talk through the various options to take a final decision. To help him,
companies are now working on development specialized chatbots that would be
able to converse with farmers and provide them with valuable facts and
analytics. Farmers’ chatbots are expected to be even smarter than
consumer-oriented Alexa and similar helpers, since they would be able not only
to give figures, but analyze them and consult farmers on tough matters.
Models Behind
Though it is
always fascinating to read about future, the most important part is the
technology that paves the way for it. Agricultural machine learning, for
instance, is not a mysterious trick or magic, but a set of well-defined models
that collect specific data and apply specific algorithms to achieve expected
results.
So far, the
distribution of machine learning is unequal throughout the agriculture. Mostly,
machine learning techniques are used in crop management processes, following
with farming conditions management and livestock management.
The literature review shows that the most
popular models in agriculture are Artificial and Deep Neural Networks (ANNs and
DL) and Support Vector Machines (SVMs).
ANNs are inspired
by the human brain functionality and represent a simplified model of the
structure of the biological neural network emulating complex functions such as
pattern generation, cognition, learning, and decision making. Such models are
typically used for regression and classification tasks which prove their
usefulness in crop management and detection of weeds, diseases, or specific
characteristics. The recent development of ANNs into deep learning that has
expanded the scope of ANN application in all domains, including agriculture.
SVMs are binary
classifiers that construct a linear separating hyperplane to classify data
instances. SVMs are used for classification, regression, and clustering. In
farming, they are used to predict yield and quality of crops as well as
livestock production.
More intricate tasks,
such as animal welfare measurement, require different approaches, such as
multiple classifier systems in ensemble learning or Bayesian models —
probabilistic graphical models in which the analysis is undertaken within the
context of Bayesian inference.
Though still in
the beginning of its journey, ML-driven farms are already evolving into
artificial intelligence systems. At present, machine learning solutions tackle
individual problems, but with further integration of automated data recording,
data analysis, machine learning, and decision-making into an interconnected
system, farming practices would change into with the so-called knowledge-based
agriculture that would be able to increase production levels and products
quality.
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