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Horticulturae
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Feature Papers in Horticulturae in 2022
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Feature Papers in Horticulturae in 2022

A special issue of Horticulturae (ISSN 2311-7524).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 20103

Special Issue Editor

Prof. Dr. Luigi De Bellis

E-Mail Website
Guest Editor
Department of Biological and Environmental Sciences and Technologies, Università del Salento, Centro Ecotekne, Via Provinciale Lecce Monteroni, 73100 Lecce, Italy
Interests: plant physiology; horticulture; biodiversity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue “Feature Papers in Developmental Physiology, Biochemistry, and Molecular Biology” aims to publish papers that highlight the results of the study of the physiology, biochemistry, and molecular biology of horticultural crops and indicate new research opportunities in achieving a better understanding of which techniques and cultivation treatments or biotechnological approaches are suitable for obtaining greater productivity along with products of higher quality for both organoleptic and sensory applications. Welcomed research may be those that encompass both in the field and in protected or post-harvest crops, including vegetables, fruit trees, vines, flowers, and ornamental, aromatic, and medicinal plants.

The intention is in fact to contribute to providing key benchmarks about the new knowledge acquired on horticultural plants and, with this help, to direct researchers towards objectives of increasing relevance in the context of a productive, quality and environmentally friendly horticulture.

The expectation is high-class submissions regarding the above topics.

Finally, the Special Issue welcomes contributions by early career researchers and proposals for specific topics.

Prof. Dr. Luigi De Bellis
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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

  • crop physiology
  • plant metabolism
  • crop molecular biology
  • plant productivity
  • crop quality

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

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25 pages, 3179 KiB  
Article
Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models
by Adel M. Al-Saif, Mahmoud Abdel-Sattar, Dalia H. Eshra, Lidia Sas-Paszt and Mohamed A. Mattar
Horticulturae 2022, 8(11), 1016; https://doi.org/10.3390/horticulturae8111016 - 1 Nov 2022
Cited by 11 | Viewed by 2939
Abstract
Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and [...] Read more.
Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for TSS, acidity, VitC, Tsugar, and Rsugar from fresh citrus fruits by applying different independent variables, namely the dimensions of the fruits (length (FL) and diameter (FD)), fruit weight (FW), yield/tree, and soil electrical conductivity (EC). The results of ANN application showed that a feed-forward back-propagation network type with four input neurons (Yield/tree, FW, FL, and FD) and eight neurons in one hidden layer provided successful modeling efficiencies for TSS, acidity, VitC, Tsugar, and Rsugar. The effect of the EC variable was not significant. The hyperbolic tangent of both the hidden layer and the output layer of the developed ANN model was chosen as the activation function. Based on statistical criteria, the ANN developed in this study performed better than the MLR model in predicting the chemical attributes of fresh citrus fruits. The root mean square error of TSS, acidity, VitC, Tsugar, and Rsugar ranged from 0.064 to 0.453 and 0.068 to 0.634, respectively, for the ANN model, and 0.568 to 4.768 and 0.550 to 4.830, respectively, for the MLR model using training and testing datasets. In addition, the relative errors obtained through the ANN approach provided high model predictability and feasibility. In chemical attribute modeling, the FD and FL variables exhibited high contribution ratios, resulting in a reliable predictive model. The developed ANN model generally showed a good level of accuracy when estimating the chemical attributes of fresh citrus fruit. Full article
(This article belongs to the Special Issue Feature Papers in Horticulturae in 2022)
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Review

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20 pages, 661 KiB  
Review
Biostimulants on Crops: Their Impact under Abiotic Stress Conditions
by Giulia Franzoni, Giacomo Cocetta, Bhakti Prinsi, Antonio Ferrante and Luca Espen
Horticulturae 2022, 8(3), 189; https://doi.org/10.3390/horticulturae8030189 - 22 Feb 2022
Cited by 97 | Viewed by 16278
Abstract
Biostimulants are agronomic tools that have been gaining importance in the reduction of fertilizer applications. They can improve the yield of cropping systems or preventing crop yield losses under abiotic stresses. Biostimulants can be composed of organic and inorganic materials and most of [...] Read more.
Biostimulants are agronomic tools that have been gaining importance in the reduction of fertilizer applications. They can improve the yield of cropping systems or preventing crop yield losses under abiotic stresses. Biostimulants can be composed of organic and inorganic materials and most of the components are still unknown. The characterization of the molecular mechanism of action of biostimulants can be obtained using the omics approach, which includes the determination of transcriptomic, proteomic, and metabolomic changes in treated plants. This review reports an overview of the biostimulants, taking stock on the recent molecular studies that are contributing to clarify their action mechanisms. The omics studies can provide an overall evaluation of a crop’s response, connecting the molecular changes with the physiological pathways activated and the performance with or without stress conditions. The multiple responses of plants treated with biostimulants must be correlated with the phenotype changes. In this context, it is also crucial to design an adequate experimental plan and statistical data analysis, in order to find robust correlations between biostimulant treatments and crop performance. Full article
(This article belongs to the Special Issue Feature Papers in Horticulturae in 2022)
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