LeafSecure: Deep Learning Detection System for Tea Leaf Diseases in Sri Lanka

Abstract

"Agriculture plays a pivotal role in Sri Lanka’s economy, notably through tea production, which is a significant source of foreign exchange earnings. However, the sector is currently grappling with a critical issue: the widespread occurrence of diseases affecting tea leaves. These diseases not only diminish the quality of the tea but also lead to a decrease in overall production. The health of the tea plants is compromised, impacting essential functions such as fertilization. In response to this problem, innovative techniques involving image processing and Convolutional Neural Networks (CNN) have been employed. The process begins with the validation of the image dataset by professionals from the Tea Research Institute (TRI). Following this, the images undergo a preprocessing phase, which includes data augmentation techniques to eliminate any background distractions and to resize the images for optimal input into the network. This step is crucial as it prepares the data for more effective analysis by the CNN. The results from the model are promising, with an accuracy score of 88%. This indicates that the model is highly effective in classifying images and identifying tea leaf diseases. However, there is room for improvement. By expanding the dataset—adding more images for the model to learn from—the accuracy and performance of the CNN-based method can be further improved. This approach holds the potential to revolutionize the identification process for tea leaf diseases, making it faster, more accurate, and less reliant on expert availability."