Abstract:
Plant disease and pest detection
machines were originally used
in agriculture and have, to some
extent, replaced traditional visual
identification. Plant diseases and
pests are important determinants of
plant productivity and quality. Plant
diseases and pests can be identified
using digital image processing.
According to the difference in
the structure of the network, this
study presents research on the
detection of plant diseases and
pests based on three aspects of the
classification network, detection
network, and segmentation network
in recent years, and summarizes
the advantages and disadvantages
of each method. A common data
set is introduced and the results of
existing studies are compared. This
study discusses possible problems
in the practical application of plant
disease and pest detection based on
deep learning.
Conventional image processing
algorithms or manual descriptive
design and classifiers are often
used for traditional computer
vision-based plant disease and pest
detection. This method usually uses
various characteristics of plant
diseases and pests to create an
image layout and selects a useful
light source and shooting angle to
produce evenly lit images.
The purpose of this work is
to identify a group of pests and
diseases of domestic and garden
plants using a mobile application
and display the final result on the
screen of a mobile device. In this
work, data from 38 different classes
were used, including diseased and
healthy leaf images of 13 plants from
plantVillage. In the experiment,
Inception v3 tends to consistently
improve accuracy with an
increasing number of epochs with no
sign of overfitting and performance
degradation. Keras with Theano
backend used to teach architectures.
