Image Processing and Pattern Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 741

Special Issue Editors


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Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: digital image processing; machine learning; evolutionary computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: digital image processing; algorithms; evolutionary computing; bio-inspired computing

E-Mail Website
Guest Editor
Department of Applied Mathematics, Chung Yuan Christian University, Taoyuan 32023, Taiwan
Interests: fuzzy clustering; machine learning and pattern recognition; image segmentation; industrial systems; statistic applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern world uses a lot of digital imaging, both motion and static, captured through various devices of all kinds, and the trend is rapidly growing. All these images must be processed, implying an understanding of the content and making decisions. Most of the digital content is ultimately irrelevant to the decision-making process but must be examined nevertheless to categorize it. While humans are still the best choice for understanding an image, the sheer amount of digital content to be processed is beyond their capabilities. Besides being a huge task, it is also a repetitive and, after all, menial task, which makes it prone to errors. This is where computers can step in to perform the menial, repetitive tasks that make up the bulk of the processing and leave only the final decision-making steps to humans.

The applications of image processing and recognition are wide and cover all aspects of social and economic activities: agriculture, weather, healthcare, public safety, surveillance, biometrics, document analysis, multimedia, driving aides, self-driving vehicles, robotics, etc.

This Special Issue aims to provide a platform to publish recent original research, review papers, or surveys in the state of the art of theoretical approaches and applications of image processing and recognition.

Topics of interest to this Special Issue include, but are not limited to, the following:

  • Image enhancement, restoration, and reconstruction;
  • Mathematical methods in image acquisition, processing, and analysis;
  • Filtering techniques;
  • Multiresolution processing;
  • Image compression;
  • Feature detection and matching;
  • Image segmentation;
  • Image registration;
  • Object classification and recognition;
  • Motion analysis;
  • Machine learning and deep learning techniques for image processing;
  • Statistical image recognition;
  • Biologically inspired methods for image processing and classification;
  • Applications.

Technical Program Committee Member
Dr. Stan Alexandru-Daniel
Affiliation: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania

Prof. Dr. Cocianu Catalina-Lucia
Dr. Cristian Răzvan Uscatu
Prof. Dr. Miin-shen Yang
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image analysis
  • image processing
  • image understanding
  • machine learning and deep learning techniques for image processing and recognition
  • evolutionary computing and bio-inspired algorithms for image processing and recognition

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Published Papers (1 paper)

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Research

22 pages, 2526 KB  
Article
An Explainable Deep Learning Framework with Adaptive Feature Selection for Smart Lemon Disease Classification in Agriculture
by Naeem Ullah, Michelina Ruocco, Antonio Della Cioppa, Ivanoe De Falco and Giovanna Sannino
Electronics 2025, 14(19), 3928; https://doi.org/10.3390/electronics14193928 - 2 Oct 2025
Cited by 1 | Viewed by 518
Abstract
Early and accurate detection of lemon disease is necessary for effective citrus crop management. Traditional approaches often lack refined diagnosis, necessitating more powerful solutions. The article introduces adaptive PSO-LemonNetX, a novel framework integrating a novel deep learning model, adaptive Particle Swarm Optimization (PSO)-based [...] Read more.
Early and accurate detection of lemon disease is necessary for effective citrus crop management. Traditional approaches often lack refined diagnosis, necessitating more powerful solutions. The article introduces adaptive PSO-LemonNetX, a novel framework integrating a novel deep learning model, adaptive Particle Swarm Optimization (PSO)-based feature selection, and explainable AI (XAI) using LIME. The approach improves the accuracy of classification while also enhancing the explainability of the model. Our end-to-end model obtained 97.01% testing and 98.55% validation accuracy. Performance was enhanced further with adaptive PSO and conventional classifiers—100% validation accuracy using Naive Bayes and 98.8% testing accuracy using Naive Bayes and an SVM. The suggested PSO-based feature selection performed better than ReliefF, Kruskal–Wallis, and Chi-squared approaches. Due to its lightweight design and good performance, this approach can be adapted for edge devices in IoT-enabled smart farms, contributing to sustainable and automated disease detection systems. These results show the potential of integrating deep learning, PSO, grid search, and XAI into smart agriculture workflows for enhancing agricultural disease detection and decision-making. Full article
(This article belongs to the Special Issue Image Processing and Pattern Recognition)
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