Authors :
Dona Joby; Christeena Shaju; Fasna T A; Avani Das
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3HZ2NnS
DOI :
https://doi.org/10.5281/zenodo.6734744
Abstract :
Most industrialised countries' economies are
based on agriculture. Crop production is one of the most
influential factors in a country's domestic market
scenario. Agricultural output is also a crucial component
of every country's economic development. Agriculture is
critical because it offers raw materials, work, and food to
a diverse population. Overuse of chemical fertilisers,
pollution of water supplies with chemicals, irregular
rainfall patterns, shifting soil fertility, and other factors
are among them. Apart from these challenges, diseaserelated loss of a significant section of output is one of the
most prominent roadblocks across the world. The
presence of illnesses in the grown plants decreases a
major share of the yield after delivering efficient
resources to the fields. As a result, scientists have been
working on a new project. As a result, scientists are
focused their efforts on developing effective ways for
detecting illness in plants. Plant diseases are a major
problem for small-scale farmers because they disrupt the
food supply. To provide efficient processes for diagnosis
and avoidance of destruction, it is necessary to identify
the kind of plant disease existing as soon as possible.
Significant progress has been achieved in discovering
plant diseases that impact a range of crops in different
regions of the world in recent years. Image capture,
preprocessing, and segmentation are all steps in the
process of detecting plant diseases. It's additionally
enhanced by a number of feature extraction and
classification methods.
Keywords :
Plant disease, VGG16, InceptionV3, Resnet50, Hybrid model.
Most industrialised countries' economies are
based on agriculture. Crop production is one of the most
influential factors in a country's domestic market
scenario. Agricultural output is also a crucial component
of every country's economic development. Agriculture is
critical because it offers raw materials, work, and food to
a diverse population. Overuse of chemical fertilisers,
pollution of water supplies with chemicals, irregular
rainfall patterns, shifting soil fertility, and other factors
are among them. Apart from these challenges, diseaserelated loss of a significant section of output is one of the
most prominent roadblocks across the world. The
presence of illnesses in the grown plants decreases a
major share of the yield after delivering efficient
resources to the fields. As a result, scientists have been
working on a new project. As a result, scientists are
focused their efforts on developing effective ways for
detecting illness in plants. Plant diseases are a major
problem for small-scale farmers because they disrupt the
food supply. To provide efficient processes for diagnosis
and avoidance of destruction, it is necessary to identify
the kind of plant disease existing as soon as possible.
Significant progress has been achieved in discovering
plant diseases that impact a range of crops in different
regions of the world in recent years. Image capture,
preprocessing, and segmentation are all steps in the
process of detecting plant diseases. It's additionally
enhanced by a number of feature extraction and
classification methods.
Keywords :
Plant disease, VGG16, InceptionV3, Resnet50, Hybrid model.
