Next-Generation Vision Systems in Agriculture—Toward Explainable and Trustworthy AI
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 2115
Special Issue Editors
Interests: deep learning; computer vision; edge computing; context-aware intelligence
Interests: computer vision; data analytics; artificial intelligence; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The MDPI journal Information invites submissions to a Special Issue on “Next-Generation Vision Systems in Agriculture — Toward Explainable and Trustworthy AI.”
The rapid evolution of AI and deep learning has revolutionized computer vision, enabling breakthroughs across diverse domains. In agriculture, these technologies are transforming how we monitor crops, detect diseases, estimate yields, manage resources, and ensure food security. However, real-world deployment in agricultural settings demands more than just accuracy—it requires robustness to environmental variability, interpretability for domain experts, multimodal integration (e.g., combining satellite, drone, and sensor data), and trustworthiness to support critical decision making.
This Special Issue aims to bring together cutting-edge research that pushes the boundaries of vision intelligence in agriculture. We invite original contributions that explore novel models, algorithms, systems, and applications that enhance the adaptability, explainability, and reliability of computer vision in complex, real-world agricultural environments.
Topics of Interest (non-exhaustive):
- Multimodal learning combining vision with text, audio, or sensor data (e.g., soil, weather, hyperspectral);
- Vision transformers and foundation models for agricultural perception tasks;
- Explainable and interpretable deep vision systems for plant disease detection, phenotyping, and yield prediction;
- Self-supervised, few-shot, and zero-shot learning for rare crop conditions and underrepresented regions;
- Robustness to adversarial attacks and domain shifts in field conditions;
- Trustworthy and ethical AI in agricultural decision support systems;
- Real-time and edge deployment of vision models on drones, robots, and IoT devices;
- Generative models (GANs, diffusion models) for synthetic data generation in agriculture;
- Human–AI collaboration in visual decision making for precision agriculture;
- Benchmark datasets, evaluation metrics, and open challenges in agricultural vision.
Dr. Alakananda Mitra
Dr. Sruti Das Choudhury
Guest Editors
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. Information is an international peer-reviewed open access monthly 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 1800 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
- artificial intelligence
- machine learning
- computer vision
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