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

Ala'a R Al-Shamasneh¹

¹Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Rafha Street, Riyadh 11586, Saudi Arabia.

|June 26, 2025

Summary

This study introduces a new method using Generalized Jones Polynomials (GJPs) for potato disease detection. The machine learning model achieved 98.45% accuracy, aiding sustainable agriculture.

Keywords:

Classification Classification of potato diseases from images Feature extraction Generalized jones polynomials Potato disease detection plant images

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate plant disease detection is crucial for sustainable agriculture and food security.
Manual disease identification is labor-intensive and requires expert knowledge.
Advances in imaging and computer vision enable quantitative plant physiology studies.

Purpose of the Study:

To develop a rapid and accurate potato disease diagnostic tool using machine learning.
To introduce a novel feature extraction method based on Generalized Jones Polynomials (GJPs).
To improve crop productivity and ensure food security through efficient disease management.

Main Methods:

Utilized the Plant Village image dataset comprising potato leaf images.
Implemented a machine learning pipeline including preprocessing, feature extraction, dimension reduction, and classification.
Employed Generalized Jones Polynomials (GJPs) for extracting texture features from images.

Main Results:

The proposed feature extraction method, combined with an Support Vector Machine (SVM) classifier, achieved a 98.45% accuracy rate in disease identification.
The methodology effectively diagnosed potato diseases from leaf images.
The approach demonstrated potential for reducing financial losses in agriculture.

Conclusions:

The novel GJP-based feature extraction technique offers a promising solution for automated potato disease diagnosis.
This method enhances the efficiency of plant disease management, contributing to increased crop yields.
The study supports the goal of ensuring global food security through technological advancements in agriculture.