Leaf Image-based Plant Disease Identification using Color and Texture Features

Abstract

Abstract Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.

Plant phenotyping relevance

葉画像から病変部位を抽出し、色・テクスチャ特徴量と分類器で植物病害状態を推定する手法の開発・検証が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper's experiments use the public PlantVillage leaf image dataset (54,309 images, 38 classes), which the authors explicitly state is available at the given GitHub URL. The authors' analysis code is only promised after acceptance, so it is not a qualifying public asset.