Automated Detection of Selected Tea Leaf Diseases by Digital Image Processing Using Convolutional Neural Network (CNN): Bangladesh Perspective

Abstract

Globally, tea production and its quality fundamentally depend on tea leaves which are susceptible to invasion from pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element to prevent and control spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy. This study focuses on a pragmatic approach for automatically detecting selected tea foliage diseases based on convolutional neural network (CNN). A large dataset of 3,330 images has been created by collecting samples from different regions of Sylhet division, the tea capital of Bangladesh. The proposed CNN model is developed based on tea leaves affected with red rust, brown blight, grey blight and healthy leaves. Afterward, the model’s prediction was validated with laboratory tests that included microbial culture media and microscopic analysis. The accuracy of this model was found to be 96.65%. Chiefly, the proposed model was developed in the context of the Bangladesh tea industry.

Plant phenotyping relevance

茶葉の病害状態を画像からCNNで自動推定する手法の開発・検証が研究の中心であり、植物表現型（病害状態）の測定に該当する。

Code and data availability

The paper's tea leaf image dataset (3,330 primary images) and CNN model are not publicly available; the Data Availability Statement says they can be obtained from the corresponding author upon reasonable request. No public repository, code, or model URL is provided.

No evidence-backed public reproduction asset is currently recorded.