Abstract:
The integration of Machine Learning (ML) in
agricultural disease prediction has become
increasingly prominent. This review paper
explores the evolution of techniques used for
predicting diseases in cinnamon leaves and
analyzes common cinnamon leaf diseases,
drawing on research conducted up to 2023. The
paper
highlights
the
evolution
of
ML
methodologies, particularly in the areas of image
processing, feature extraction, and classification
algorithms. It provides an in-depth analysis of
various approaches, such as Convolutional
Neural Networks (CNNs), Support Vector
Machines (SVMs), and Random Forests,
evaluating
their
effectiveness
in
disease
prediction. From an initial set of 100 studies, 22
were selected for detailed analysis based on their
relevance and contribution to the field.
Additionally, the review addresses the challenges
associated with developing reliable ML models.
Through the synthesis of findings from multiple
studies, this paper offers a comprehensive
overview of current research in cinnamon leaf
disease and prediction, identifying existing gaps
and proposing directions for future investigations
to improve the precision and applicability of ML
driven solutions in agriculture.
