DISEASE PREDICTION OF APPLE LEAF USING MACHINE LEARNING

Abstract

Current trends of farming is
having a gap to identify the problem
upfront even before capturing the
results. Similar kind of farming to figure
out the problem in farming is apple leaf,
instead of finding the problem in later
time one need a background in
understanding the disease prediction for
apple leaf. Considering the factors in
effecting the plant growth, image
classifications and features between
correct abstraction of images, will help
us in identifying the apple leaf plant
growth. Image classification methods in
Machine learning such as Convolutional
Neural network emphasis the detailed
regression algorithms to predict the
plant disease upfront in order to fix the
plant with relevant methods. Apple leaf
disease is captured with every stage of
images of the plants and ensured we ran
through lot of train and test datasets to
train our algorithm in picturizing the
early sign of apple leaf diseases. The
number of datasets images have to run
through the algorithm of convolution
neural network and artificial neural
network, in identifying the early
predictions of apple leaf diseases. The
SVM algorithm helps in understanding
the classification of any image across
each stage of lifecycle of apple plant. The
further of analysis gives a greater
flexibility on training the algorithm and
its impact across the plant, the final
accuracy of
identification of any kind of prediction of
apple leaf disease and ensuring the
required methods to be taken is higher
accuracy in compared to other models.