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 pre-trained transfer learning approach in future studies.