Plant leaf disease detection using local binary pattern and deep convolutional neural networks

Abstract

Plants are susceptible to pathogen infections during their growing period leading to reduced crop quality and yield. Traditional disease detection methods such as expert diagnosis and pathogen analysis rely on experienced professionals and could be time-consuming and prone to errors. Deep convolutional neural networks (CNNs) have exhibited their potential to detect plant diseases on the basis of visual patterns of leaves. Most of the existing CNN based methods do not take advantage of additional information. Most of the disease significantly affects the texture of the plant leaves. Therefore, texture features can provide complementary information to get better results. In this paper, local binary pat tern (LBP) technique is used to extract texture information that is stacked with original image. A CNN model is proposed that takes embedded texture and spectral information to detect crop diseases using leaf images. The experiments are carried out on Apple, Corn, and Potato crops from Plant Village dataset. The proposed method achieved the overall accuracy up to 98.73% (κ = 98.04). It is found that LBP makes significant difference in disease classification accuracy and helps the proposed method exhibit better performance than some existing well known CNN models.

Plant phenotyping relevance

葉画像から植物病害状態を推定するLBP＋CNN手法を開発・評価しており、植物表現型（病害）の取得・抽出が中心である。

Code and data availability

The paper uses the public PlantVillage leaf image dataset for its experiments, but no author-specific dataset deposit, analysis code, trained model, or supplement with reproducible assets is mentioned. No availability statement or authors' public URL for code/data appears in the supplied blocks; the only URLs are the期刊

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