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Plants
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Modelling for Prediction of Horticultural Plant Growth and Defense
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Modelling for Prediction of Horticultural Plant Growth and Defense

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2651

Special Issue Editor

Dr. Maryam Alavi-Shoshtari

E-Mail Website
Guest Editor
New Zealand Institute of Plant and Food Research, Private Bag 92169, Auckland Mail Centre, Auckland 1142, New Zealand
Interests: Applied mathematical modelling of plant physiology; Biological dynamical systems; Stochastic differential equations; Bayesian inference; Signal processing; Decision theory; Optimisation and control

Special Issue Information

Dear Colleagues,

The horticulture industry focuses on providing high-quality products, free of disorders, to consumers while achieving optimum profit for the suppliers and minimum waste. For this purpose, plant growth and defence mechanisms have been of particular interest in the scientific domain; models describing the dynamics of plant growth provide a vision for optimising practices across the supply chain and mitigating disorder risks and disease outbreaks. Furthermore, advances in data collection and monitoring technologies, as well as increased computational capabilities, are making complex models feasible that integrate biological mechanisms of plants with environmental factors and human-led activities in the context of smart horticulture and digital twins of horticultural systems.

This Special Issue of Plants will focus on comprehensive models to describe or predict plant growth and defence, in addition to prescriptive approaches to modelling to optimize outcomes. The articles will consider advances in several aspects of growth, from identifying factors in development or defence mechanisms in biological models within the plant to the study of the dynamics and interactions between plants and environmental factors, including soil or weather conditions, for example. These modelling approaches will cover the spectrum from biological or physiological systems to phenomenological or empirical data-centric methods such as machine learning or artificial intelligence.

Dr. Maryam Alavi-Shoshtari
Guest Editor

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

  • smart horticulture
  • growth or defence mechanisms in plants
  • dynamical systems of plant growth/defence
  • integrated models in plant growth
  • prediction of horticultural systems by AI (artificial intelligence)
  • computational biology
  • plant–environment system dynamics
  • prediction models with regular monitoring

Published Papers (3 papers)

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Research

21 pages, 5025 KiB  
Article
Utilizing Two Populations Derived from Tropical Maize for Genome-Wide Association Analysis of Banded Leaf and Sheath Blight Resistance
by Shaoxiong Li, Fuyan Jiang, Yaqi Bi, Xingfu Yin, Linzhuo Li, Xingjie Zhang, Jinfeng Li, Meichen Liu, Ranjan K. Shaw and Xingming Fan
Plants 2024, 13(3), 456; https://doi.org/10.3390/plants13030456 - 04 Feb 2024
Viewed by 889
Abstract
Banded leaf and sheath blight (BLSB) in maize is a soil-borne fungal disease caused by Rhizoctonia solani Kühn, resulting in significant yield losses. Investigating the genes responsible for regulating resistance to BLSB is crucial for yield enhancement. In this study, a multiparent [...] Read more.
Banded leaf and sheath blight (BLSB) in maize is a soil-borne fungal disease caused by Rhizoctonia solani Kühn, resulting in significant yield losses. Investigating the genes responsible for regulating resistance to BLSB is crucial for yield enhancement. In this study, a multiparent maize population was developed, comprising two recombinant inbred line (RIL) populations totaling 442 F8RILs. The populations were generated by crossing two tropical inbred lines, CML444 and NK40-1, known for their BLSB resistance, as female parents, with the high-yielding but BLSB-susceptible inbred line Ye107 serving as the common male parent. Subsequently, we utilized 562,212 high-quality single nucleotide polymorphisms (SNPs) generated through genotyping-by-sequencing (GBS) for a comprehensive genome-wide association study (GWAS) aimed at identifying genes responsible for BLSB resistance. The objectives of this study were to (1) identify SNPs associated with BLSB resistance through genome-wide association analyses, (2) explore candidate genes regulating BLSB resistance in maize, and (3) investigate pathways involved in BLSB resistance and discover key candidate genes through Gene Ontology (GO) analysis. The GWAS analysis revealed nineteen SNPs significantly associated with BLSB that were consistently identified across four environments in the GWAS, with phenotypic variation explained (PVE) ranging from 2.48% to 11.71%. Screening a 40 kb region upstream and downstream of the significant SNPs revealed several potential candidate genes. By integrating information from maize GDB and the NCBI, we identified five novel candidate genes, namely, Zm00001d009723, Zm00001d009975, Zm00001d009566, Zm00001d009567, located on chromosome 8, and Zm00001d026376, on chromosome 10, related to BLSB resistance. These candidate genes exhibit association with various aspects, including maize cell membrane proteins and cell immune proteins, as well as connections to cell metabolism, transport, transcriptional regulation, and structural proteins. These proteins and biochemical processes play crucial roles in maize defense against BLSB. When Rhizoctonia solani invades maize plants, it induces the expression of genes encoding specific proteins and regulates corresponding metabolic pathways to thwart the invasion of this fungus. The present study significantly contributes to our understanding of the genetic basis of BLSB resistance in maize, offering valuable insights into novel candidate genes that could be instrumental in future breeding efforts to develop maize varieties with enhanced BLSB resistance. Full article
(This article belongs to the Special Issue Modelling for Prediction of Horticultural Plant Growth and Defense)
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15 pages, 2807 KiB  
Article
A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation
by Roman Y. Pishchalnikov, Denis D. Chesalin, Vasiliy A. Kurkov, Uliana A. Shkirina, Polina K. Laptinskaya, Vasiliy S. Novikov, Sergey M. Kuznetsov, Andrei P. Razjivin, Maksim N. Moskovskiy, Alexey S. Dorokhov, Andrey Yu. Izmailov and Sergey V. Gudkov
Plants 2023, 12(24), 4178; https://doi.org/10.3390/plants12244178 - 15 Dec 2023
Viewed by 720
Abstract
The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings [...] Read more.
The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings to the early detection of fungal infection in major agro-industrial crops and products. So, we propose a prototype method, the key feature of which is a procedure analyzing the lineshape of a spectrum. The shape of the absorption spectrum corresponding to this transition strongly depends on the immediate environment of a pigment and can serve as a marker to detect the presence of a particular pigment molecule in a sample. Considering carotenoids as an object of study, we demonstrate that the combined operation of the differential evolution algorithm and semiclassical quantum modeling of the optical response based on a generalized spectral density (the number of vibronic modes is arbitrary) allows us to distinguish quantum models of the pigment for different solvents. Moreover, it is determined that to predict the optical properties of monomeric pigments in protein, it is necessary to create a database containing, for each pigment, in addition to the absorption spectra measured in a predefined set of solvents, the parameters of the quantum model found using differential evolution. Full article
(This article belongs to the Special Issue Modelling for Prediction of Horticultural Plant Growth and Defense)
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17 pages, 4708 KiB  
Article
Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water
by Fernando Ferrari Putti, Camila Pires Cremasco, Alfredo Bonini Neto, Ana Carolina Kummer Barbosa, Josué Ferreira da Silva Júnior, André Rodrigues dos Reis, Bruno César Góes, Bruna Arruda and Luís Roberto Almeida Gabriel Filho
Plants 2023, 12(22), 3811; https://doi.org/10.3390/plants12223811 - 09 Nov 2023
Viewed by 714
Abstract
Due to the worldwide water supply crisis, sustainable strategies are required for a better use of this resource. The use of magnetic water has been shown to have potential for improving irrigation efficacy. However, a lack of modelling methods that correspond to the [...] Read more.
Due to the worldwide water supply crisis, sustainable strategies are required for a better use of this resource. The use of magnetic water has been shown to have potential for improving irrigation efficacy. However, a lack of modelling methods that correspond to the experimental results and minimize error is observed. This study aimed to estimate the replacement rates of magnetic water provided by irrigation for lettuce production using a mathematical model based on fuzzy logic and to compare multiple polynomial regression analysis and the fuzzy model. A greenhouse study was conducted with lettuce using two types of water, magnetic water (MW) and conventional water (CW), and five irrigation levels (25, 50, 75, 100 and 125%) of crop evapotranspiration. Plant samples for biometric lettuce were taken at 14, 21, 28 and 35 days after transplanting. The data were analyzed via multiple polynomial regression and fuzzy mathematical modeling, followed by an inference of the models and a comparison between the methods. The highest biometric values for lettuce were observed when irrigated with MW during the different phenological stage evaluated. The fuzzy model provided a more exact adjustment when compared to the multiple polynomial regressions. Full article
(This article belongs to the Special Issue Modelling for Prediction of Horticultural Plant Growth and Defense)
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