Abstract:
Diseases have remained as one of the major issues affecting dragon fruit production due to
its susceptibility to infections. The framework of this research work is the use of a deep
learning technique for an assessment of disease infections in dragon fruits with data
sourced from a local nursery. The dataset comprises images annotated with four target
attributes. A dataset of 2000 high-resolution images, captured under various lighting
conditions from local nursery and farms. After Augmentation and Labeling 4,827 images
was annotated with four target attributes. New-germination (1300), Germination-white
(1203), Heavy red germination (1200), and Wilting (1124). Five models: Xception,
VGG19, InceptionV3, MobileNetV2 and one custom CNN model was used for the
classification of diseases. Among those, the highest accuracy level was recorded at 93.49
% by using MobileNetV2, while the other three models: Xception, VGG19, InceptionV3
achieved 91.01%, 76.80% and 86.04%, respectively. The result of the complex metric in
MobileNetV2 outperforms all other methods significantly while the good performance of
the custom CNN which achieved an impressive 91.82% proves that it is an eminently
suitable solution designed specifically to act on this task. This implementation helps
farmers be able to identify diseases from their crops early enough to reduce crop loss and
at the same time help reduce the use of chemicals. Future research could consider several
directions for improving the use of deep learning in the identification of dragon fruit
diseases. Exploring the possibility of using techniques, in which several deep learning
models are employed simultaneously, may prove beneficial to increase the general
reliability of the results
