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Electronics
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Artificial Intelligence and Image Processing in Smart Agriculture
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Artificial Intelligence and Image Processing in Smart Agriculture

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 644

Special Issue Editor

Dr. Muhammad Umer Khan

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Guest Editor
Department of Mechatronics Engineering, Atilim University, Ankara 06830, Türkiye
Interests: robotics; agriculture; machine learning; deep learning; precision agriculture; crop monitoring and disease detection

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to exploring the transformative role of artificial intelligence (AI) and image processing technologies in the domain of smart agriculture. As global agricultural systems face increasing demands for efficiency, sustainability, and resilience, intelligent solutions are becoming essential.

The primary aim of this Special Issue is to showcase recent advancements and innovative applications that leverage AI and image-based solutions to address real-world challenges in agriculture. We invite contributions from researchers, practitioners, and domain experts across multidisciplinary areas who are pioneering the use of these technologies to enhance productivity, reduce labor costs, monitor crop health, optimize resource utilization, and support decision-making processes.

We particularly encourage submissions that not only demonstrate technical innovation but also emphasize practical implementations and measurable impact on agricultural practices. This Special Issue aspires to serve as a comprehensive reference for ongoing research and to establish guidelines for future investigations in this rapidly evolving field. The topics of interest include, but are not limited to, the following:

  • AI-based crop monitoring and disease detection using computer vision;
  • Precision agriculture using UAV/drone imagery and remote sensing;
  • Smart irrigation systems supported by AI and visual analytics;
  • Yield estimation and prediction using deep learning models;
  • Pest detection through image processing or artificial intelligence;
  • Multispectral/hyperspectral image analysis for soil and crop health;
  • Integration of IoT, AI, and imaging for real-time agricultural monitoring;
  • Digital Twin models for simulating and optimizing agricultural operations

Dr. Muhammad Umer Khan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • smart agriculture
  • artificial intelligence
  • deep learning
  • image processing
  • precision agriculture
  • remote sensing
  • crop monitoring
  • pest and disease detection
  • yield prediction
  • hyperspectral imaging
  • multispectral imaging
  • smart irrigation
  • agricultural robotics

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Published Papers (1 paper)

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Research

27 pages, 6664 KB  
Article
Advancing Multi-Label Tomato Leaf Disease Identification Using Vision Transformer and EfficientNet with Explainable AI Techniques
by Md. Nurullah, Rania Hodhod, Hyrum Carroll and Yi Zhou
Electronics 2025, 14(23), 4762; https://doi.org/10.3390/electronics14234762 - 3 Dec 2025
Viewed by 456
Abstract
Plant diseases pose a significant threat to global food security, affecting crop yield, quality, and overall agricultural productivity. Traditionally, diagnosing plant diseases has relied on time-consuming visual inspections by experts, which can often lead to errors. Machine learning (ML) and artificial intelligence (AI), [...] Read more.
Plant diseases pose a significant threat to global food security, affecting crop yield, quality, and overall agricultural productivity. Traditionally, diagnosing plant diseases has relied on time-consuming visual inspections by experts, which can often lead to errors. Machine learning (ML) and artificial intelligence (AI), particularly Vision Transformers (ViTs), and Convolutional Neural Networks, offer a faster, automated alternative for identifying plant diseases through leaf image analysis. However, these models are often criticized for their “black box” nature, limiting trust in their predictions due to a lack of transparency. Our findings show that incorporating Explainable AI (XAI) techniques, such as Grad-CAM, Integrated Gradients, and LIME, significantly improves model interpretability, making it easier for practitioners to identify the underlying symptoms of plant diseases. This study not only contributes to the field of plant disease detection but also offers a novel perspective on improving AI transparency in real-world agricultural applications through the use of XAI techniques. With training accuracies of 100.00% for ViT, 96.88% for EfficientNetB7, 93.75% for EfficientNetB0, and 87.50% for ResNet50, and corresponding validation accuracies of 96.39% for ViT, 86.98% for EfficientNetB7, and 82.00% for EfficientNetB0, our proposed models outperform earlier research on the same dataset. This demonstrates a notable improvement in model performance while maintaining transparency and trustworthiness through interpretable and reliable decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence and Image Processing in Smart Agriculture)
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