Plant leaf vein and outline feature extraction using fractal and computer vision approaches

Abstract

The study focuses on utilizing plant leaf characteristics for plant identification and disease detection. Leaves are pivotal for gathering information about plants. The proposed model uses computer vision and smart agricultural technologies to discern venation and texture features in various plant leaves. This research utilized a modified dataset derived from the Flavia leaf image dataset, comprising images of 32 plant species. The dataset was divided into two subsets (one with 1907 images and another with 1000 images) to differentiate between tuned and untuned image processing. Techniques such as GLCM, LBP, Gabor filters, Fractal Dimension, and box-counting were employed to extract leaf texture features, including venation patterns. The study conducted four experiments with training and testing splits of 70/30 and 80/20. A novel method combining SVM with fractal dimension analysis was benchmarked against six classifiers (Random Forest, KNN, DNN, Naïve Bayes, Decision Tree, andSVM), achieving an impressive accuracy of 88% and a Fractal Dimension of 1.8709. This research holds significant potential for advancing digital and modern agriculture, particularly in the early detection of plant diseases and accurate plant identification.

Plant phenotyping relevance

葉の輪郭・葉脈・テクスチャ特徴を画像から抽出する手法の開発と分類器比較が研究の中心であり、植物器官の観測可能な形態特徴を定量化しているため含める。

Code and data availability

The paper uses the public Flavia leaf dataset (a pre-existing external dataset, not a paper-specific deposit) and describes GLCM/LBP/Gabor/fractal-dimension feature extraction and six classifiers, but provides no availability statement, deposit, or URL for its own code, extracted feature values, trained models, or data

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