Classification of Cassava Leaf Diseases using Deep Gaussian Transfer
In Sub-Saharan Africa, Professionals visually analyse the plants by
looking for disease markers on the leaves to diagnose cassava
infections, however, this method is extremely subjective. Automating the
identification and classification of crop diseases may improve the
accuracy of professional disease diagnosis and enable farmers in remote
areas to monitor their crops without the assistance of experts.
Algorithms for machine learning have been used in the early detection
and classification of crop diseases. Motivated by the current
developments in the field of Gaussian Processes, this study proposes to
integrate the transfer learning approach with a deep Gaussian
convolutional neural network model (DGCNN) for the detection and
classification of cassava diseases. During this study, we used MobileNet
V2 and VGG16 pre-trained transfer learning models and a hybrid kernel.
Experiments with MobileNet V2 and a hybrid kernel revealed an accuracy
of 90.11%. Also, experiments with VGG16 and a hybrid kernel revealed an
accuracy of 88.63%. The major limitation of this study was computing
resources since we used an ordinary computer in all our experiments. In
our future work, we will experiment with the three kernel functions used
in this study with kernel algorithms such as support vector machines and
compare the results with those obtained during this study.