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Agriculture
Special Issues
Recent Advances in Modern Seed Technology
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Recent Advances in Modern Seed Technology

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Seed Science and Technology".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 12781

Special Issue Editors

Prof. Dr. Arthur Novikov

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Guest Editor
Forest Engineering Faculty, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8, Timiryazeva, 394087 Voronezh, Russia
Interests: seed technology; seed testing; seed grading; seed quality; seeding; seed drying; seed pelleting; seed enhancement; ontogenesis from seeds
Dr. Clíssia Barboza Mastrangelo

E-Mail Website
Guest Editor
Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba 13416-000, SP, Brazil
Interests: non-destructive methods; seed quality; imaging technology; multispectral imaging; radiography; fluorescence; seed-borne fungi
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paweł Tylek

E-Mail Website1 Website2
Guest Editor
Faculty of Forestry, University of Agriculture in Krakow, 31425 Krakow, Poland
Interests: physical characteristics of materials; forest biomass; agrophysics; mechanization of nursery works; machinery construction; automation and robotization of production processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In agriculture and forestry, everything that is sown in the soil is commonly called seeds. Morphologically, these can be not only fruits, but also infructescence (for example, beet) or, on the contrary, fruit lobes, the so-called eremas (mint, perilla, sage and other labiate families), as well as a part of the fruit, for example, the bone of a number of fruit tree species. These can also be, finally, fractional fruits (mericarpium), i.e., decaying longitudinally along the partitions into lobes corresponding to one fruit leaf (umbellate).

In the vast majority of plant species, seed formation is the final stage of ontogenesis. The seeds that arose during the long evolution of plant organisms concentrated the signs of the species and acquired various adaptations that enabled reproducing their own kind. For heterotrophic organisms—animals and humans—seeds are an essential source of nutrition, since they contain proteins and other nitrogenous products, lipids, carbohydrates, vitamins, etc. It is not surprising, therefore, that seeds have long attracted the attention of researchers from various fields. Geneticists and breeders use seeds to produce new, more productive and economically valuable varieties of various agricultural and forest crops. Plant growers strive to increase the yield of seeds and improve their quality by means of various measures. The whole chain of processes occurring in the forming seeds, the specifics of their maturation and subsequent dormancy, the patterns of seed germination and their transformation into a new plant have constantly been in the field of view of plant physiologists.

However, seeds as objects of research are also important for technologists. For technologists, seeds are the basis of the technological processes of testing, the identification of substandard seeds, grading, activation, pelletizing, seeding and other processes carried out using modern techniques and technical means.

This Special Issue is focused on (but not limited to) modern technologies in seed production, and will cover the following headings:

Headings (expansion is possible)

  • Seed physiology
  • Seed enhancement (seed collecting; seed grading by: spectrometric features, size, form, acoustic features, etc.; seed pelleting; seed drying; seed activation by: low-intensity laser radiation, growth stimulants, microwaves; seed priming by: osmosis, hydration, etc.)
  • Seed quality (seed testing by: nuclear magnetic resonance, infrared optical beam, multispectral imaging, UV optical beam, biospeckle analysis, autofluorescence, X-ray imaging, ultrasonic, etc.)
  • Plant ontogenesis from enhancement seeds (Seeding and seeder; Planting and planter)

Prof. Dr. Arthur Novikov
Prof. Dr. Clíssia Barboza Mastrangelo
Prof. Dr. Paweł Tylek
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. Agriculture 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 2600 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

  • seed technology
  • seed biology
  • seed enhancement
  • seed testing
  • seed grading
  • seed quality
  • seeding
  • seed pelleting
  • optical imaging
  • multispectral imaging
  • X-ray imaging
  • magnetic resonance
  • machine learning
  • plant ontogenesis from seeds

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Published Papers (4 papers)

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Research

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19 pages, 3617 KiB  
Article
Wheat Seed Phenotype Detection Device and Its Application
by Haolei Zhang, Jiangtao Ji, Hao Ma, Hao Guo, Nan Liu and Hongwei Cui
Agriculture 2023, 13(3), 706; https://doi.org/10.3390/agriculture13030706 - 18 Mar 2023
Cited by 3 | Viewed by 2021
Abstract
To address the problem of low efficiency and automatically sense the phenotypic characteristics of wheat seeds, a wheat seed phenotype detection device was designed to predict thousand seed weight. Five commonly used varieties of wheat seeds were selected for the study, and a [...] Read more.
To address the problem of low efficiency and automatically sense the phenotypic characteristics of wheat seeds, a wheat seed phenotype detection device was designed to predict thousand seed weight. Five commonly used varieties of wheat seeds were selected for the study, and a wheat seed phenotype detection system was built with a 2 mm sampling hole plate. Grayscale, image segmentation, area filtering and other methods were used to process the image in order to extract and analyse the correlation between thousand seed weight and seven phenotypic characteristics: wheat seed area, perimeter, long axis, short axis, ellipticity, rectangularity, and elongation. The results showed that different varieties of wheat seeds were significantly correlated with different phenotypic characteristics. Among them, the area and short axis for Luomai 26; the area, long axis, short axis, perimeter, and rectangularity for Jinqiang 11; the area and perimeter for Zhoumai 22; the area of Luomai 42; the area, short axis, and perimeter for Bainong 207 were significantly correlated with the thousand seed weight. A multiple linear regression model of thousand seed weight prediction was developed by selecting the significantly correlated phenotypic characteristic. The models showed that the R2 values of the thousand seed weight prediction models for Jinqiang 11 and Bainong 207 were 0.853 and 0.757, respectively; and the R2 values for Luomai 26, Zhoumai 22, and Luomai 42 were less than 0.5. Subsequently, PCA-MLR was used to build a thousand seed weight prediction model, and K-fold cross-validation was used for comparative analysis. Afterwards, three kinds of wheat seeds with 40–50 g thousand seed weight were selected to validate the model. The validation results showed that the more significantly correlated the phenotypic parameters were, the higher the accuracy of the thousand seed weight prediction model. The study provided a set of detection devices and methods for the rapid acquisition of the phenotypic characteristics of wheat seeds and thousand seed weight prediction. Full article
(This article belongs to the Special Issue Recent Advances in Modern Seed Technology)
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17 pages, 2258 KiB  
Article
How Can the Engineering Parameters of the NIR Grader Affect the Efficiency of Seed Grading?
by Tatyana P. Novikova, Clíssia Barboza Mastrangelo, Paweł Tylek, Svetlana A. Evdokimova and Arthur I. Novikov
Agriculture 2022, 12(12), 2125; https://doi.org/10.3390/agriculture12122125 - 10 Dec 2022
Cited by 8 | Viewed by 1728
Abstract
The automated grading of Scots pine seeds in the near-infrared wavelength region (NIR grading) is a starting point for further actions, such as coating and priming. This reduces the time and financial costs and increases the accuracy of seed viability classification compared to [...] Read more.
The automated grading of Scots pine seeds in the near-infrared wavelength region (NIR grading) is a starting point for further actions, such as coating and priming. This reduces the time and financial costs and increases the accuracy of seed viability classification compared to invasive techniques. The NIR-based wave reflected from each pine seed must be detected and processed with sufficient accuracy. To focus the reflected beam, we used fiber-optic Bragg grating, a Bragg mirror, and diffraction grating. For each focusing option based on the DOE matrix, one experiment of 20 runs (n = 20) and three replicas (m = 3) in each run was conducted. In each replica, we used 100 conditioned and 100 non-conditioned seeds (NC + NNC = 200) selected randomly from five samples weighing 50 g from a seedlot weighing 1 kg extracted from cones collected from a natural tree stand. Three experiments were conducted on the NIR grading of Scots pine seeds using an optoelectronic device. An adequate DOE regression model of the grading efficiency function was obtained. The functions included the following arguments: angle of incidence of the optical beam, NIR wavelength reflected from the seed, and height of the seed pipeline. The influence of the inclination angle of the light source relative to the plane of pine seed movement on the grading quality prevails over other factors. The NIR grading of Scots pine seeds allows the separation of seeds according to the viability index, which is important, since dead petrified seeds (possibly up to 25%) may occur in the seed batch, which cannot be eliminated by either seed size or mass. The peak of NIR grading is achieved by combining the average grader engineering parameters: 968–973 nm for the wavelength and 44–46 degrees for the inclination angle of the reflected beam at a seed pipe size of 0.18–0.23 m. Full article
(This article belongs to the Special Issue Recent Advances in Modern Seed Technology)
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14 pages, 2983 KiB  
Article
Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology
by Rodrigo Cupertino Bernardes, André De Medeiros, Laercio da Silva, Leo Cantoni, Gustavo Ferreira Martins, Thiago Mastrangelo, Arthur Novikov and Clíssia Barboza Mastrangelo
Agriculture 2022, 12(11), 1801; https://doi.org/10.3390/agriculture12111801 - 29 Oct 2022
Cited by 12 | Viewed by 2695
Abstract
Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by Fusarium head blight (FHB) can help to prevent human and animal health risks while improving agricultural sustainability. Robust pattern-recognition methods, such as deep learning, can achieve higher precision in detecting [...] Read more.
Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by Fusarium head blight (FHB) can help to prevent human and animal health risks while improving agricultural sustainability. Robust pattern-recognition methods, such as deep learning, can achieve higher precision in detecting infected seeds using more accessible solutions, such as ordinary RGB cameras. This study used different deep-learning approaches based on RGB images, combining hyperparameter optimization, and fine-tuning strategies with different pretrained convolutional neural networks (convnets) to discriminate wheat seeds of the TBIO Toruk cultivar infected by FHB. The models achieved an accuracy of 97% using a low-complexity design architecture with hyperparameter optimization and 99% accuracy in detecting FHB in seeds. These findings suggest the potential of low-cost imaging technology and deep-learning models for the accurate classification of wheat seeds infected by FHB. However, FHB symptoms are genotype-dependent, and therefore the accuracy of the detection method may vary depending on phenotypic variations among wheat cultivars. Full article
(This article belongs to the Special Issue Recent Advances in Modern Seed Technology)
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Review

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25 pages, 1829 KiB  
Review
Physiological Alterations and Nondestructive Test Methods of Crop Seed Vigor: A Comprehensive Review
by Muye Xing, Yuan Long, Qingyan Wang, Xi Tian, Shuxiang Fan, Chi Zhang and Wenqian Huang
Agriculture 2023, 13(3), 527; https://doi.org/10.3390/agriculture13030527 - 22 Feb 2023
Cited by 11 | Viewed by 3807
Abstract
Seed vigor is one of the essential contents of agricultural research. The decline of seed vigor is described as an inevitable process. Recent studies have shown that the oxidative damage caused by reactive oxygen species (ROS) is the main reason for the destruction [...] Read more.
Seed vigor is one of the essential contents of agricultural research. The decline of seed vigor is described as an inevitable process. Recent studies have shown that the oxidative damage caused by reactive oxygen species (ROS) is the main reason for the destruction of various chemicals in seeds and eventually evolves into seed death. The traditional vigor tests, such as the seed germination test and TTC staining, are commonly used to assess seed vigor. However, these methods often need a large number of experimental samples, which will bring a waste of seed resources. At present, many new methods that are fast and nondestructive to seeds, such as vibrational spectroscopic techniques, have been used to test seed vigor and have achieved convincing results. This paper is aimed at analyzing the microchanges of seed-vigor decline, summarizing the performance of current seed-vigor test methods, and hoping to provide a new idea for the nondestructive testing of a single seed vigor by combining the physiological alterations of seeds with chemometrics algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Modern Seed Technology)
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