| dc.description.abstract | Objective: This research study aims to develop an efficient deep-learning
model to detect and classify stages of Black Sigatoka disease in banana
plants. Methods: In this study, deep learning techniques, specifically the basic
Convolutional Neural Network (CNN) and VGG16 models, were used to address
the challenge of identifying Black Sigatoka disease in banana leaves early
on. The tests were conducted on a dataset containing labelled images of
banana leaves, assessing their effectiveness based on criteria such as accuracy,
precision, recall, and F1-score after adjusting hyperparameters for optimal
outcomes. Findings: The results of the trials revealed that the basic CNN
model attained a training accuracy of 96% and a validation accuracy of 89%,
surpassing the performance of the VGG16 model. The VGG16 model, on the
other hand, had a training accuracy of 92% and a validation accuracy of
89%. Across precision, recall, and F1 score measurements, the basic CNN
model consistently outperformed the VGG16 model, with scores averaging
0.90 for all three metrics compared to VGG16’s precision of 0.80, recall of
0.75, and F1 score of 0.75. The CNN model demonstrated its efficiency by
stopping training at 26 epochs, whereas VGG16 completed training in 21
epochs. This demonstrates its effectiveness in detecting Black Sigatoka while
utilising minimal resources. Novelty: A significant component of this study
is its emphasis on identifying the stages of Black Sigatoka disease, which is
commonly overlooked in research. By studying disease progression, this study
provides insights for early intervention and disease management, aiding efforts
to lessen the impact of Black Sigatoka on banana farming. | en_US |
