Abstract:
One of the most serious plant diseases to the production of tea is the tea leaf disease. In
the early stages, it often shows no visible symptoms, making early detection difficult until
the infection spreads across the leaf. As a result, tea leaf disease is considered a leading
cause of crop loss in tea plantations. This paper suggests that deep learning models,
specifically the transfer learning and the ensemble approach, could be deployed in the
automation of tea leaf disease detection. First of all, the dataset was pre-treated, and
several data augmentation methods were implemented, such as resizing, rescaling,
flipping, rotation, zooming, and contrast adjustment.We then conducted Error Level
Analysis (ELA) to identify any patterns that may have been overlooked in the images. The
research explored deep learning models capable of accurately distinguishing between
different classes of the disease. Pre-trained models such as EfficientNetB0 and
EfficientNetV2B1 were employed for transfer learning. Furthermore, ensemble models
combining CNN with EfficientNetB0 and CNN with EfficientNetV2B1 were evaluated. All
models were tested using two approaches: ordinal classification and regular classification.
Among the transfer learning models, EfficientNetB0 achieved the highest accuracy of
91.91% with ordinal classification. Within the ensemble models, CNN+ EfficientNetB0
reached a peak accuracy of 89.04% under ordinal classification. These models can assist
agronomists and tea farmers, particularly those with limited resources, by enabling
effective identification and classification of tea leaf disease
