Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network

Hafijur Rahman; Iftekhar Ahmad; Parvej Hasan Jon; Abdus Salam; Md. Forhad Rabbi

doi:10.1038/s41598-024-62058-3

journal article Open Access Jun 18, 2024

Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network

Hafijur Rahman Iftekhar Ahmad Parvej Hasan Jon Abdus Salam

Md. Forhad Rabbi

Scientific Reports Vol. 14 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/s41598-024-62058-3

Abstract

AbstractGlobally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling the 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 3330 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 by 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.

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Published: Jun 18, 2024
Vol/Issue: 14(1)
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Cite This Article

Hafijur Rahman, Iftekhar Ahmad, Parvej Hasan Jon, et al. (2024). Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-62058-3

Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network

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