Optimum RBM encoded SVM model with ensemble feature Extractor-based plant disease prediction

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-01-11 DOI:10.1016/j.chemolab.2025.105319

Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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引用次数: 0

Abstract

In agricultural technology, accurate and speedy plant disease identification is essential to maintain the optimum crop quality and output. This research proposed a system that can automatically diagnose diseases in apple fruit and apple trees using machine learning (ML) image processing. Thus, this research offers a novel approach for accurate plant disease prediction by combining an Ensemble Feature Extractor with an Optimum Restricted Boltzmann Machine (RBM) Encoded Support Vector Machine (SVM) model. The model uses RBM-encoded features and SVM classification, and several feature extraction techniques enhance it. The experiments across the PDD271 dataset with 220,592 images and 271 categories demonstrate the model's outstanding classification performance, stressing its potential to develop agricultural technology and enable early disease diagnosis for better crop management. Consequently, with respective values of 98 %, 98 %, 89.7 %, and 97.8 %, the model may give more successful outcomes regarding accuracy, precision, recall, and F1 Score.

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基于集合特征的最优RBM编码支持向量机模型

在农业技术中，准确和快速的植物病害鉴定对于保持最佳作物品质和产量至关重要。本研究提出了一种利用机器学习（ML）图像处理技术自动诊断苹果果实和苹果树疾病的系统。因此，本研究将集成特征提取器与最优受限玻尔兹曼机（RBM）编码支持向量机（SVM）模型相结合，为植物病害的准确预测提供了一种新的方法。该模型采用rbm编码特征和SVM分类，并采用多种特征提取技术对其进行增强。通过对PDD271数据集的220,592张图像和271个类别的实验，证明了该模型出色的分类性能，强调了其在发展农业技术和实现早期疾病诊断以更好地进行作物管理方面的潜力。因此，在分别为98%、98%、89.7%和97.8%的情况下，该模型可能会在准确性、精密度、召回率和F1分数方面给出更成功的结果。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.