Towards precision agriculture tea leaf disease detection using CNNs and image processing.

Area of Science:

• Agricultural Science
• Computer Science
• Plant Pathology

Background:

• Tea production faces significant losses due to various diseases.
• Accurate and early disease detection is crucial for effective management.
• Traditional methods for disease identification can be labor-intensive and subjective.

Purpose of the Study:

• To develop and evaluate a deep learning model for classifying common tea leaf diseases.
• To leverage advanced image analysis for automated disease diagnosis.
• To improve the accuracy and efficiency of disease detection in tea plants.

Main Methods:

• A deep learning model with a multi-layer architecture was designed for 256x256 RGB images.
• The model incorporates convolutional layers, residual blocks, batch normalization, and ReLU activation.
• GlobalAveragePooling2D, dropout, and dense layers were used for feature extraction and classification.
• A dataset of 4000 high-resolution tea leaf images was collected and utilized for training and validation.

Main Results:

• The deep learning model achieved remarkable accuracy in identifying diseases in tea leaves.
• The architecture effectively extracts intricate patterns and handles spatial information.
• Residual blocks helped in training deeper networks, mitigating the vanishing gradient problem.

Conclusions:

• The developed deep learning model offers a precise and automated solution for tea leaf disease classification.
• This research sets a new benchmark for precision in agricultural diagnostics.
• The findings pave the way for advancements in precision agriculture and disease management strategies.