Deep Learning & Transfer Learning: CNNs for Plant Disease Detection, Classification, and Technological Innovation
Guest editors:
Michael Gomez Selvaraj, Alliance of Bioversity International and International Center for Tropical Agriculture, Colombia
Junfeng Gao, University of Aberdeen, United Kingdom
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Articles
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Hyperspectral imaging for pest symptom detection in bell pepper
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Study on canopy extraction method for narrowband spectral images based on superpixel color gradation skewness distribution features
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Variation in forest root image annotation by experts, novices, and AI
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Measurement of maize stalk shear moduli
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Plant diseases and pests detection based on deep learning: a review
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Protocol: a simple method for extracting next-generation sequencing quality genomic DNA from recalcitrant plant species
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Simple extraction methods that prevent the artifactual conversion of chlorophyll to chlorophyllide during pigment isolation from leaf samples
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Plant genome editing made easy: targeted mutagenesis in model and crop plants using the CRISPR/Cas system
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Comparison of three genomic DNA extraction methods to obtain high DNA quality from maize
Collections
2024
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.
2023
Bioactive Natural Products from Plants: Analysis and Association with Human Health
Plants are rich in various bioactive molecules, including polyphenols, carotenoids, phytosterols, tocopherols, tocotrienols, organosulfur compounds, peptides, and fibers. These bioactive compounds have been proven to promote human health by preventing the onset and progression of diseases.
Development of New Sensing Technology in Sustainable Farming and Smart Environmental Monitoring
In recent years, new sensing technology and instruments, artificial intelligence, big data and Internet of Things have developed rapidly. The application of such technology and instruments has permeated almost all scientific research and production practices in all walks of life. Not unexpectedly, it has already had a profound impact on plant science.
Speed Breeding in Crops
In the scenario of global climate change, food security is a critical issue due to the increasing human population and environmental pollutants, and one of the greatest challenges is how to accelerate plant breeding programs for future crops with high yield and stress tolerance.
2022
Accelerating image-based plant phenotyping and pattern recognition: deep learning or few-shot learning?
In recent years, deep learning methods have played a great role in the plant sciences and achieved a series of remarkable achievements in many fields. Few-shot learning is a new branch of deep learning, which aims to develop an intelligent model with good generalization from only few data, towards the combination of machine intelligence with flexibility and extensibility. Both deep learning and few-shot learning are technological explorations in the field of plant sciences that have the potential to greatly accelerate their applications.
2021
Deep learning or few-shot learning?
Few-shot learning is a new branch of deep learning, which aims to develop an intelligent model with good generalization from only few data, towards the combination of machine intelligence with flexibility and extensibility. Both deep learning and few-shot learning are technological explorations in the field of plant sciences that have the potential to greatly accelerate their applications.
2018
Plants in computer vision
This collection includes work on plant detection, segmentation and modelling from image data, at many different scales.
2016
Plant genome editing
This series focuses on gene editing as an effective tool in plant biotechnology and genetic engineering.
2014
Plant phenotyping and phenomics
This series covers all aspects of phenotyping and phenomics technologies as applied to plant research and includes and includes a number of invited contributions from speakers at the 3rd International Plant Phenotyping Symposium.
2013
Next Generation Sequencing technologies for plant research
This series focuses on the use of different sequencing technologies that allow us to sequence DNA and RNA much more quickly and cheaply than Sanger sequencing and their application to plant research.
Aims and scope
Plant Methods is an open access, peer-reviewed journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences.
Technological innovation is probably the most important catalyst for progress in any scientific discipline. The goal of this journal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
Editor-in-Chief
Daniel Dias, Deakin University
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About the Editor-in-Chief
Daniel Dias, Editor-in-Chief
Dr Dan Dias is a Senior Lecturer in Analytical Biochemistry within the Centre for Advanced Sensory Science (CASS) and the newly established Australian Research Council – Industrial Transformation Training Centre (HyTECH), School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University. He has over 15 years of experience in analytical biochemistry, metabolomics and natural product chemistry. Dr Dias has focused on the application of hyphenated separation technologies to multidisciplinary fields ranging from: analytical wine chemistry, plant metabolism, micro and macro algae, food ripening, age-related macular degeneration, ecology, coral bleaching, food technologies, cheese flavour and quality, Diabetes Mellitus, herbal medicine, and transgenic Buffalo grasses. His current research focuses on investigating the maintenance of flavor and the alterations that occur in food products throughout their storage duration; applying metabolomics to assess the impact of Polyphenol Rich Sugarcane Extracts (PRSE) on inflammatory cell signalling pathways and identifying bioactive-natural products from marine organisms to alleviate food spoilage. In 2019 he was awarded the inaugural International Metabolomics Society President’s Award in recognition encompassing scientific excellence, impact in teaching and service in the community. To date, Dr Dias has co-edited 1 book, authored several book chapters, 70+ peer-reviewed journal articles with 6800+ citatations and a current h-index = 34 and has recently been appointed the Editor in Chief for the journal Plant Methods.
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Annual Journal Metrics
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Citation Impact 2023
Journal Impact Factor: 4.7
5-year Journal Impact Factor: 5.6
Source Normalized Impact per Paper (SNIP): 1.401
SCImago Journal Rank (SJR): 0.956Speed 2023
Submission to first editorial decision (median days): 5
Submission to acceptance (median days): 112Usage 2023
Downloads: 1,429,395
Altmetric mentions: 2,040