Several diseases threaten the coconut sector by impacting the well-
being and production of coconut trees. This affects the quality and
sustainability of coconut cultivation in India. It is vital to promptly and
precisely identify diseases to implement practical management
approaches. In this study, we used an unbalanced coconut tree disease
dataset from Mendley and applied data augmentation techniques to
minimize the imbalance and improve the model’s robustness. To
classify the diseases accurately, we explored, customized, and trained
five deep learning models-VGG19, InceptionV3, DenseNet201,
Xception, and ResNet50. Among these, InceptionV3 achieved the
highest performance across all metrics, with an accuracy of 99% and a
Cohen’s Kappa value of 0.96. Furthermore, we compared the
performance of our best model against existing methods,
demonstrating that our approach outperforms previous works.
P.S. Pravitha, V. Kumar
Central University of Kerala, India
Deep Learning, Coconut Disease Detection, Pre-Trained Models, Transfer Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 3 , Pages: 3639 - 3645 )
Date of Publication :
January 2025
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