Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model.

Abstract

Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.

Plant phenotyping relevance

キュウリ葉の病斑・病害状態を対象に、ハイパースペクトル画像から病害を認識するECR分類モデルを開発・評価しており、植物表現型（病害状態）の取得・判定法が中心である。

Code and data availability

The paper describes hyperspectral cucumber leaf disease data and an ECR classification model, but no blocks contain any data availability statement, public repository deposit, or author code/model release. The hyperspectral images and spectral curves are not stated as publicly available, and no qualifying paperSpecific

No evidence-backed public reproduction asset is currently recorded.