Sparse Representation-based Plant Disease Detection using Leaf Image Processing

Abstract

Existing image-based crop disease recognition approaches commonly determine multiple feature types from images of diseased plant leaves. Yet, a shared limitation remains: those discriminative features are commonly assumed to contribute uniformly to the classification decision. In this study, we present an apple leaf disease recognition pipeline composed of three sequential modules: segmenting diseased leaf images through K-means clustering, deriving shape and color features from the diseased region, and then performing classification of diseased leaf images via the sparse representation (SR) method. One notable strength of this pipeline is that conducting classification within the SR domain can notably decrease computational load while simultaneously boosting recognition accuracy. We benchmark this method against three baseline classifiers - support vector machines (SVM), artificial neural networks (ANN), and decision tree (DT) - using a leaf image dataset containing three apple leaf disease categories: black spot, frogeye leaf spot, and cedar apple rust. Experimental results indicate that the presented pipeline attains 93.9% overall accuracy, whereas the SVM, ANN, and DT achieve 76.8%, 83.7%, and 82.9%, respectively.

Plant phenotyping relevance

リンゴ葉の病斑領域を画像から分割・特徴抽出し、病害状態を分類する画像ベースの植物フェノタイピング手法を開発・比較しており、方法が中心である。

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