Feature Selection using intersection of SVM-RFE and ARFA with ABi-LSTM Classification for Paddy Plant Leaf Disease

Abstract

Abstract The major aim of the study is to determine some of the diseases affecting rice plants which lead to crop loss. The suggested model is designed into four primary phases, i.e., pre-processing, feature extraction, feature selection, and classification. The proposed models have been applied to two sets of data that include Rice Disease and the Rice Leaf Disease Image that constitute a total of four types of classes of the paddy leaves: healthy, blast, bacterial blight, and tungro. The dataset images are initially upgraded in the first stage of pre-processing in order to enhance the quality of the images. A Gaussian filter is used to eliminate noise on the green spectral band and to convert the input images to RGB color space. The step second involves deriving color and texture information out of each of the pre-processed images. The third step Features selection SVM-RFE has been used to select features with the help of the intersection with the ARFA technique. The fourth step involves a final step where the features chosen are applied to classify the kind of disease existing in each picture. ABi-LSTM model is used in the classification process and the accuracy is 97.05.

Plant phenotyping relevance

イネ葉画像から病害状態を推定する画像ベースの表現型解析手法を、前処理・特徴抽出・特徴選択・分類のワークフローとして開発しており、方法が中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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