Enhancing Generalization in Agricultural AI: Bridging Data Gaps and Boosting Model Robustness
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 22
Special Issue Editors
Interests: digital signal processing; digital audio processing; digital image processing; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning on imagery in precision agriculture; deep learning; semantic segmentation; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning models in agriculture often struggle with limited generalization capabilities due to challenges such as data scarcity, variability in environmental conditions, and domain shifts between training and deployment scenarios. These limitations can lead to reduced model performance when faced with new or unseen data, hindering the adoption of AI-driven technologies in precision agriculture. Addressing these challenges requires innovative approaches that enhance model robustness, adaptability, and scalability.
The goal of this Special Issue is to explore advanced strategies for improving the generalization capability of machine learning models in agriculture. This includes techniques such as data augmentation, domain adaptation, transfer learning, and multimodal data fusion, as well as the use of synthetic data, generative models (e.g., GANs, VAEs), and Large Language Models (LLMs) to overcome data limitations. The scope of this Special Issue covers diverse agricultural applications, including crop monitoring, soil and water management, pest and disease detection, and yield prediction.
This Special Issue will highlight studies on innovative methods to enhance model generalization, such as leveraging diverse data sources, improving training pipelines, evaluating model robustness under real-world conditions, and developing adaptive models that maintain performance across varying environments and tasks.
We are seeking original research articles, case studies, technical notes, and reviews that present novel approaches, practical applications, and insights into building more generalizable and resilient AI models for agriculture.
Dr. Jayme Garcia Arnal Barbedo
Dr. Mulham Fawakherji
Guest Editors
Manuscript Submission Information
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Keywords
- generalization in machine learning
- precision agriculture
- domain adaptation
- transfer learning
- data augmentation
- synthetic data generation
- multimodal data fusion
- generative models (GANs, VAEs)
- large language models (LLMs)
- robustness and adaptability
- crop monitoring and management
- soil and water analysis
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