CLASSIFICATION OF GRAPE LEAF IMAGES USING MACHINE LEARNING TECHNIQUES

Abstract

Grape is one of the most widely consumed fruit worldwide.There are several diseases present in grape leaf that reduces its yield regularly.Its early and accurate detection is crucial for effective yield outcomes.Classification techniques provided high potential for assisting farmers.This paper presents a four-class classification approach for grape leaf image classification using medium gaussian Support Vector Machine (SVM).SVMs are one of the powerful supervised learning models for classification task.In the proposed approach, features are extracted from grape leaf images from Niphad dataset.The images provide information for distinguishing data in four categories.The texture features of images like Difference theoretic texture features (DTTF), First Order Statistics (FOS), Fractal Texture (FT), Grey Level Difference Statistics (GLDS), Statistical Feature Matrix (SFM), Local Binary Patterns (LBP), Segmentation Based Fractal Texture Analysis (SFTA) and Tamura features are used to form a feature set for classification.In the proposed methodology evaluation is done using accuracy and sensitivity.The results are observed to outperform the state of the art methods.

Plant phenotyping relevance

ブドウ葉画像から病害状態を分類する画像ベースの表現型推定手法が研究の中心であり、特徴抽出、SVM分類、精度・感度評価を実施している。

Code and data availability

The paper uses the Niphad grape leaf image dataset and MATLAB-based SVM classification, but provides no public URL, deposit, or availability statement for the dataset, code, or trained model. The only URLs in the text are the DOI and citations to prior work, none of which are authors' assets for this paper.

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