Tobacco being one of the perennial crops that
are the major source of forex in Malawi. But due to the
decrease in agricultural extension workers specializing in
Tobacco this paper proposes a remedy for tobacco disease
detection and grading using a convolution neural network
(CNN). This paper proposes the use of CNN as a machine
learning algorithm for the training a system to detect
tobacco diseases while grading the tobacco. This paper
also compares CNN algorithm with other neural network
algorithms in terms of sensitivity, specificity and other
parameters such that the CNN proves to be efficient and
effective hence offering higher performance of the
algorithms. This research work will be designing and
developing a learning model, which will classify, grade
and detect the disease in the tobacco plant leaf by using
the convolutional neural network based on the type of the
tobacco Leaf. As these convolutional networks work with
a large number of images for the training of the model,
real-time images will be collected from the reliable source
such as Agricultural Research and Extension Trust
(ARET) and Tobacco Auction Flow Point respectively.
Keywords :
Image Processing, Convolutional Neural Network (CNN), Leaf Detection, Agricultural Research and Extension Trust (ARET), Tobacco Classification, Tobacco Grading