Potato leaves disease classification based on generalized Jones polynomials image features.

Abstract

The detection of plant diseases in the modern era offers a promising first step toward sustainable agriculture and food security. Plant physiology can be studied quantitatively thanks to advances in imaging and computer vision. Conversely, manual interpretation requires a great deal of labor, knowledge of plant diseases. Numerous innovative methods for identifying and classifying particular diseases have been widely used. In order to diagnose potato diseases more accurately and quickly using a machine learning model, this study uses a new feature extraction method based on GJPs image features. The methodology of this study relies on:•Modules for preprocessing, feature extraction, dimension reduction, and classification.•Generalized jones polynomials as new image features method is used to extract the texture features from potato images for diagnosing potato diseases. The data used in this model were collected from the plant village image dataset using samples of potato leaves. Using an SVM classifier on potato leaf images, the disease was accurately identified in 98.45 % of cases. The recommended feature extraction technique can reduce financial loss while also assisting in the efficient management of plant diseases, enhancing crop productivity and ensuring food security.

Plant phenotyping relevance

ジャガイモ葉画像から病害状態を推定する画像特徴抽出・分類手法が研究の中心であり、植物病害フェノタイピング手法として適格。

Code and data availability

The paper reports potato leaf disease classification using GJPs features on PlantVillage images with MATLAB classifiers, but provides no public code, models, or processed data. The only availability statement is 'Data will be made available on request.' The PlantVillage dataset itself is a cited external resource, nota

No evidence-backed public reproduction asset is currently recorded.