Bayesian Optimization of Convolutional Neural Network Model in
Prediction of Cassava Diseases Using Spectral Data
Abstract
Food security depends on the early detection of agricultural diseases,
particularly in Sub-Saharan Africa. Professionals visually evaluate the
plants by searching for disease indications on the leaves to diagnose
cassava infections, a notoriously subjective process. Farmers in remote
areas may be able to monitor their crops without the assistance of
specialists if crop diseases are automatically detected and classified.
This could aid in the more accurate diagnosis of diseases by
professionals. Crop disease classification and early identification have
benefited from the application of machine learning techniques. Despite
their excellent accuracy, there is no single machine learning model that
can provide optimal results on all the datasets. In this study, a
Bayesian optimization of the Convolutional Neural Network (CNN) model
was proposed. The model was trained using the spectral dataset. Since
spectral data is highly dimensional, dimensionality reduction was
performed on the dataset using PCA. The experimental results revealed
that the proposed model had an accuracy of 85.19%, Precision of
85.23%, Recall of 85.16%, and F1 score of 85.20%. Also, the proposed
model had an AUC of 0.95 which demonstrates excellent performance.
However, there is still a need to improve the overall performance of the
proposed model and we recommend the use of a pretrained transfer
learning approach in future studies.