  The detection of diseases within plants has attracted a lot of attention from
computer vision enthusiasts. Despite the progress made to detect diseases in
many plants, there remains a research gap to train image classifiers to detect
the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao
plants. This gap has mainly been due to the unavailability of high quality
labeled training data. Moreover, institutions have been hesitant to share their
data related to CSSVD. To fill these gaps, we propose the development of image
classifiers to detect CSSVD-infected cacao plants. Our proposed solution is
based on VGG16, ResNet50 and Vision Transformer (ViT). We evaluate the
classifiers on a recently released and publicly accessible KaraAgroAI Cocoa
dataset. Our best image classifier, based on ResNet50, achieves 95.39\%
precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20
epochs. There is a +9.75\% improvement in recall when compared to previous
works. Our results indicate that the image classifiers learn to identify cacao
plants infected with CSSVD.
