Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves.

Abstract

This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.

Plant phenotyping relevance

ナス葉の病徴をハイパースペクトル画像とテクスチャ特徴から検出する画像解析手法が研究の中心であり、植物病害状態の表現型推定に該当する。

Code and data availability

The article describes hyperspectral imaging of eggplant leaves and GLCM texture/KNN/AdaBoost analysis, but contains no public dataset, image, code, or model deposit. The only URL present is the CC-BY license notice; no availability statements or author-provided public links appear in any block.

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