Deep Learning & Transfer Learning: CNNs for Plant Disease Detection, Classification, and Technological Innovation
Published in Computational Sciences and Plant Science
Plant Methods is calling for submissions to our Collection on "Deep Learning & Transfer Learning: CNNs for Plant Disease Detection, Classification, and Technological Innovation". The advancement of deep learning and transfer learning techniques, particularly convolutional neural networks (CNNs), has revolutionized the field of plant disease detection and classification. These innovations have enabled the development of sophisticated image processing algorithms that can accurately identify and classify plant diseases, leading to improved crop management and agricultural sustainability. As we continue to make strides in this area, it is crucial to understand the importance of these advancements.
Significant advances have already been made in this field, with the application of CNNs and transfer learning leading to remarkable improvements in plant disease detection and classification accuracy. These technologies have enabled the automation of disease diagnosis, reducing the reliance on manual inspection, and significantly expediting the identification of plant diseases. Furthermore, the integration of deep learning techniques with traditional plant science disciplines has facilitated a more comprehensive understanding of plant-pathogen interactions and disease mechanisms. Looking ahead, the potential for further advances in this area is vast. Continued research and innovation in deep learning and transfer learning are expected to lead to the development of more robust and interpretable CNN models tailored specifically for plant disease detection. Additionally, the integration of multi-modal data, including spectral and temporal information, with CNN-based approaches holds promise for enhancing the accuracy and reliability of disease classification. Furthermore, the exploration of transfer learning methodologies across different plant species and diseases is anticipated to yield generalized models with broader applicability, thereby contributing to the development of scalable solutions for diverse agricultural settings.
The Collection seeks to showcase cutting-edge research in deep learning and transfer learning, particularly focusing on the application of convolutional neural networks for plant disease detection, classification, and technological innovation. We invite contributions that explore novel methodologies, innovative applications, and interdisciplinary approaches in this rapidly evolving field. Sub-topics may include but are not limited to:
- Novel CNN architectures for plant disease detection
- Transfer learning approaches for plant disease classification
- Integration of multi-modal data for improved disease detection
- Interdisciplinary approaches combining deep learning with traditional plant science disciplines
- Automation of disease diagnosis and its impact on agricultural sustainability
-Generative AI approaches (like LLMs) for plant disease research
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Plant Methods
Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences.
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Engineering Plant Metabolism for Enhancing Carotenoid and Polyphenolic Production: RNAi, Genome Editing, and Multi-Omics
The field of plant metabolic engineering has witnessed remarkable advancements in recent years, particularly in the enhancement of carotenoid and polyphenolic compound production. These bioactive compounds are not only vital for plant health but also play critical roles in human nutrition, offering a wealth of natural products that can combat various health issues. As our understanding of the genetic and biochemical pathways governing these metabolites expands, so too does the potential for innovative agricultural and nutritional strategies. Recent breakthroughs in RNA interference (RNAi) and genome editing techniques have allowed scientists to fine-tune gene expression, resulting in increased levels of carotenoids and polyphenolic compounds in various plant species. Furthermore, multi-omics approaches, which integrate genomics, transcriptomics, proteomics, and metabolomics, have provided unprecedented insights into the metabolic pathways involved in the biosynthesis of these pigments. Such innovations not only enhance our comprehension of plant biology but also facilitate the development of crops with improved nutritional profiles and resilience to environmental stressors. Looking ahead, the potential for future advancements in carotenoid and polyphenolic production is vast. We may witness the emergence of engineered plants that can thrive in challenging environments while simultaneously producing higher concentrations of valuable metabolites. Additionally, the ongoing refinement of CRISPR and other gene-editing technologies could lead to more precise modifications in metabolic pathways, resulting in crops that are not only nutritionally superior but also environmentally sustainable. The integration of synthetic biology into plant metabolic engineering holds promise for the creation of entirely novel pathways, enabling the production of unique bioactive compounds that could have significant applications in medicine and industry.
We invite researchers to contribute to this special Collection, which aims to gather innovative studies that will further our understanding of carotenoid and polyphenolic production through RNA interference and multi-omics. By sharing your findings, you can play a crucial role in advancing the field of plant metabolic engineering and its applications for global health and sustainability.
Topics of interest include but are not limited to:
- Employing RNA interference and genome editing to manipulate carotenoid biosynthesis
- Multi-omics approaches for polyphenolic profiling
- Engineering natural products for enhanced health benefits
- Pigment accumulation in response to environmental stress
- Metabolic pathways of carotenoids and polyphenolic
- Bioactive compounds in plant-derived foods
- Applications of synthetic biology in metabolic engineering
- Innovations in crop biofortification strategies
This Collection supports and amplifies research related to SDG 2: Zero Hunger.
All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.
Publishing Model: Open Access
Deadline: May 29, 2026
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