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For
fastly increasing population, increasing of crop production is a
challenging task to meet the human requirement and to resolve the
demands of the global population. The statistics says that the crop
production is increasing in an average rate of 1.3% annually which is
not upto the pace to meet the demand of human population since the
demand expects a rate of 2.4%. To ensure the improvement in the
productivity of crops, plant phenotyping holds a major role in terms
of determining and identification of plant properties. The plant
phenotype relies on the feature extraction of fine-grained traits
such as crop color, height, architecture which are differentiate from
other plants that is varying under different environmental
conditions. Early days, this categorization was done manually by
scientists, but recent days the approach is improved by image based
technologies. In last decades, several image processing advanced
techniques are introduced to measure the plant traits which can
categorize the visual traits of plants. However, the lack of access
to the phenotypic information and capabilities still block the
ability to extract the genetic information of quantitative traits
related to yeild, growth, tolerance, architecture and adaptation of
biotic and abiotic stress. However, the translation of data into the
identification
of desirable traits is a constraint since there is a
lack of knowledge of
the associated phenotypes. To avoid this
bottleneck and to avail atmost benefit from the available
information, multi-functional, reliable, automated and
high-throughput phenotyping platforms to be developed and deployed to
give a improved perspective to the plant scientists. In recent years,
high throughput phenotyping platforms are deployed in greenhouses and
growth chambers with deticated controlled environment. Although the
high throughput phenotyping platforms give the detailed fine-grained
trait information in overall to the plant life cycle, the result from
controlled environment are distinct from the actual field where the
plants will experience in different situations and stress.

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These
high
throughput platforms
use precise environmental control and imaging technologies to assess
plant
growth
and architecture.
However, these platforms are designed for a limited range of species,
including
small
rosette plants such as Arabidopsis,
Tobacco
and the primary cereal crops. This
has to be improved by enabling the evaluation of simultaneous
phenotypic of
different species. Field based Phenotyping (FBP) is the advanced
technique to meet the required improvement in throughput and gives an
accurate assessment of field crops. Two different types of FBP
vehicles are deployed over years (1) Ground
wheeled vehicles and
(2) Aerial
vehicles which
are equipped with different sesnsors to accomplish to feature
extraction over plot to plot in field crops. In
the recent years, the cable-suspended
field
based
phenotyping
platform provides
a safety and high accurate measure of plant traits whereas covering a
large region of crops is relatively low which limit its application
in large scale area. Satellite
imaging technologies can
resolve the high area coverage limitation and useful for
collecting data for various agricultural applications. However, it
limits due to the hig cost of currently
available satellite sensors and
the risk
of cloudy imagery and have long periods to revisit the same region.
This
lack the
spatial resolution for the identification
of
desirable traits. To
overcome large scale crop monitoring and provide high spectral
resolutions sensors, Manned
airborne platforms have introduced
with different sensors equipping platform capable
to give high spatial and spectral resolutions.
However, high operating
costs
and the operational complexity have limited the use of
these
platforms.