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Plants
Special Issues
Plant Stress and Machine Learning
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Plant Stress and Machine Learning

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 2308

Special Issue Editors

Prof. Dr. Xiaohui Wu

E-Mail Website
Guest Editor
Pasteurien College, Soochow University, Suzhou 215000, China
Interests: RNA processing; multi-omics analysis; bioinformatics
Dr. Liuyin Ma

E-Mail Website
Guest Editor
Haixia Institute of Science and Technology, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Interests: RNA processing; nutrient signaling; stress resistance
Dr. Yuchen Yang

E-Mail Website
Guest Editor
State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Shenzhen 518000, China
Interests: plant genomics; environmental adaptation; molecular regulation; multi-omics investigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plants are sessile, and environmental stresses are important limiting factors for plant growth and yield production. Plant stress is thus a defensive state in which plants are growing under non-ideal environmental conditions, caused by various biotic (e.g., bacterial, fungal, viral diseases, and insect) and/or abiotic (e.g., drought, salinity, temperature, and nutrient extremes) factors. Ambient stresses affect the growth and development of crops and cause serious losses in crop yields, posing a growing threat to global food security. Plant adaptation to stresses is accomplished through interacting biochemical or metabolic pathways, molecular mechanisms, and physiological traits. Technological advances in plant science have generated extensive multi-omics datasets, including genomics, transcriptomics, proteomics, metabolomics, and phenomics. As such, machine learning (ML) methods are particularly crucial for integrating multi-omics data for plant stress research.

This Special Issue aims to gain a more comprehensive and in-depth understanding of the molecular mechanisms underlying plant defensive responses to different types of stresses. This issue welcomes the submission of articles highlighting the development and application of ML algorithms on multi-omics data for plant stress research into the identification, classification, or measurement of plant stress, prediction of plant stress at an early stage, or plant stress phenotyping.

Prof. Dr. Xiaohui Wu
Dr. Liuyin Ma
Dr. Yuchen Yang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Plants 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 2700 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

  • plant stress
  • machine learning
  • deep learning
  • computational biology
  • multi-omics
  • big data
  • non-model organisms

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Published Papers (1 paper)

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Research

24 pages, 28576 KiB  
Article
Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach
by Reem Ibrahim Hasan, Suhaila Mohd Yusuf, Mohd Shafry Mohd Rahim and Laith Alzubaidi
Plants 2023, 12(8), 1603; https://doi.org/10.3390/plants12081603 - 10 Apr 2023
Cited by 11 | Viewed by 1775
Abstract
The current methods of classifying plant disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share [...] Read more.
The current methods of classifying plant disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%. Full article
(This article belongs to the Special Issue Plant Stress and Machine Learning)
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