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Application of Imaging and Artificial Intelligence in Seed Research
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Application of Imaging and Artificial Intelligence in Seed Research

A special issue of Seeds (ISSN 2674-1024).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3944

Special Issue Editors

Dr. Ewa Ropelewska

E-Mail Website
Guest Editor
Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland
Interests: seed biology; biodiversity; storage; seed quality; nondestructive quality evaluation; image analysis; spectroscopy; machine vision; cultivar discrimination; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Kadir Sabancı

E-Mail Website1 Website2
Guest Editor
Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey
Interests: deep learning; image processing; artificial intelligence; seed quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of imaging and artificial intelligence allows the monitoring of seed quality in an objective, effective, and non-destructive manner. Nowadays, the importance of rapid and non-destructive procedures in seed quality assessment is constantly increasing. Approaches that combine image processing and traditional machine learning or deep learning techniques are often used as an alternative to destructive, time-consuming, subjective, or expensive measurements. Various imaging techniques, such as multispectral and hyperspectral imaging, digital imaging, laser-induced light backscattering imaging, fluorescence imaging, Raman imaging, X-ray computed tomography, magnetic resonance, microwave imaging, or thermal imaging, can be useful to extract information about the external or internal structures of seeds. Image features can provide valuable data about seed characteristics that may be invisible to the naked eye. Selected image features can be used to develop models using different machine learning algorithms to distinguish different seed samples and predict seed quality attributes. These models can be effective at identifying varieties and species of seeds, breeding programs, assessing the effects of cultivation conditions on the seed quality, seed grading and sorting, assessing the effects of storage and processing on seed quality, and detecting seed abnormality, defects, or diseases. Researchers that have dealt with all aspects of the use of imaging and artificial intelligence in seed research are highly encouraged to submit their reviews or research papers.

Dr. Ewa Ropelewska
Dr. Kadir Sabancı
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Seeds is an international peer-reviewed open access quarterly 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 1000 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

  • digital imaging
  • spectral imaging
  • tomography
  • thermal imaging
  • image processing
  • traditional machine learning
  • deep learning
  • seed quality

Published Papers (4 papers)

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Research

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13 pages, 3465 KiB  
Article
Study of Grapevine (Vitis vinifera L.) Seed Morphometry and Comparison with Archaeological Remains in Central Apennines
by Valter Di Cecco, Aurelio Manzi, Camillo Zulli, Michele Di Musciano, Angelo Antonio D’Archivio, Marco Di Santo, Guido Palmerini and Luciano Di Martino
Seeds 2024, 3(3), 311-323; https://doi.org/10.3390/seeds3030023 - 27 Jun 2024
Viewed by 211
Abstract
Studying the evolution of seed morphology and, in turn, the evolution of cultivars across time and space is of fundamental importance to agriculture and archaeology. The identification of ancient and modern grapevine (Vitis vinifera L.) cultivars is essential for understanding the historical [...] Read more.
Studying the evolution of seed morphology and, in turn, the evolution of cultivars across time and space is of fundamental importance to agriculture and archaeology. The identification of ancient and modern grapevine (Vitis vinifera L.) cultivars is essential for understanding the historical evolution of grape cultivation. Grape seed morphology provides valuable information to explore the evolution of grape cultivars over time and space. The main aim of our study was to build a comprehensive regional database of grape seed morphological traits from modern and archaeological wine cultivars and wild grape species. We aimed to identify which seeds of modern grape cultivars exhibited morphological similarities to archaeological cultivars. This study focused on fifteen distinct modern types of seeds and two archaeological samples from the Byzantine-to-Early Medieval period. We acquired digital images of seeds using a flatbed scanner. For each sample, 100 seeds were randomly selected, and morphometric data on each seed were gathered using ImageJ. Differences among the seed cultivars were investigated using linear discriminant analysis. Archaeological seeds were found to be more similar to cultivated V. vinifera cultivars rather than V. sylvestris populations. Among the cultivated cultivars, Sangiovese and Tosta antica resulted to be cultivars most similar cultivars to the archaeological ones. The morphometric analysis of grape seeds proved to be a valuable resource for investigating the evolution of vine cultivars throughout history. Combining image analysis techniques with genetic data will open new perspectives for studying the origins of and variations in grape cultivars, contributing to the conservation and enhancement of viticultural heritage. Full article
(This article belongs to the Special Issue Application of Imaging and Artificial Intelligence in Seed Research)
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25 pages, 6692 KiB  
Article
Morphometric Analysis of Grape Seeds: Looking for the Origin of Spanish Cultivars
by Francisco Emanuel Espinosa-Roldán, José Luis Rodríguez-Lorenzo, José Javier Martín-Gómez, Ángel Tocino, Víctor Ruiz Martínez, Adrián Remón Elola, Félix Cabello Sáenz de Santamaría, Fernando Martínez de Toda, Emilio Cervantes and Gregorio Muñoz-Organero
Seeds 2024, 3(3), 286-310; https://doi.org/10.3390/seeds3030022 - 24 Jun 2024
Viewed by 308
Abstract
The Vitis IMIDRA collection contains 3699 entries, representing a significant percentage of the variation in traditional and commercial Vitis cultivars used in Spain. The classification and identification of new entries are currently conducted based on ampelography and molecular methods. Here, we propose a [...] Read more.
The Vitis IMIDRA collection contains 3699 entries, representing a significant percentage of the variation in traditional and commercial Vitis cultivars used in Spain. The classification and identification of new entries are currently conducted based on ampelography and molecular methods. Here, we propose a new method of classification of the cultivars based on seed morphology and its application to a total of 224 varieties from the collection. Based on seed shape, fourteen groups have been defined according to the similarity of the seeds, with geometric figures used as models. The new models are Cariñena Blanca, Chardonnay, Parraleta, and Parduca, defining new groups to be added to the ten groups previously described. The study results in 14 groups comprising the Spanish cultivar’s seed shape and morphological variation. Seed morphology can help to identify varieties cultivated in the past through archaeological finds. Full article
(This article belongs to the Special Issue Application of Imaging and Artificial Intelligence in Seed Research)
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16 pages, 3569 KiB  
Article
Geometric Analysis of Seed Shape Diversity in the Cucurbitaceae
by José Javier Martín-Gómez, Diego Gutiérrez del Pozo, José Luis Rodríguez-Lorenzo, Ángel Tocino and Emilio Cervantes
Seeds 2024, 3(1), 40-55; https://doi.org/10.3390/seeds3010004 - 31 Dec 2023
Cited by 1 | Viewed by 978
Abstract
The Cucurbitaceae is a monophyletic family with close to 1000 species of climbers, including important agronomic species and varieties characterized by tendrils and pepo fruits. The seed’s morphology is varied, and the development and structure of the seed coat have been described in [...] Read more.
The Cucurbitaceae is a monophyletic family with close to 1000 species of climbers, including important agronomic species and varieties characterized by tendrils and pepo fruits. The seed’s morphology is varied, and the development and structure of the seed coat have been described in detail on some species. Overall description of the seed shape is based on terms comparing it with geometric figures, but quantitative methods are lacking in the literature. Here we apply a general morphological analysis to seeds of representative genera of the Cucurbitaceae, followed by curvature analysis in the poles and symmetry analysis. These methods are useful for the quantitative description of seed shape and the comparison between species and varieties. Differences between species were found for most morphological measurements, as well as for symmetry and curvature values. The comparison between three species of Cucumis (Cucumis sativus, C. myriocarpus and C. melo) and two varieties of C. melo reveals differences between species and varieties both in curvature and symmetry. The results obtained from both methods, curvature and symmetry analysis, form similar grou**s in a cluster analysis. The methods described here were applied for the identification of agronomic varieties and the quantitative description of seed shape in taxonomy. Full article
(This article belongs to the Special Issue Application of Imaging and Artificial Intelligence in Seed Research)
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Review

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17 pages, 2571 KiB  
Review
Deep Learning for Soybean Monitoring and Management
by Jayme Garcia Arnal Barbedo
Seeds 2023, 2(3), 340-356; https://doi.org/10.3390/seeds2030026 - 15 Aug 2023
Cited by 1 | Viewed by 1437
Abstract
Artificial intelligence is more present than ever in virtually all sectors of society. This is in large part due to the development of increasingly powerful deep learning models capable of tackling classification problems that were previously untreatable. As a result, there has been [...] Read more.
Artificial intelligence is more present than ever in virtually all sectors of society. This is in large part due to the development of increasingly powerful deep learning models capable of tackling classification problems that were previously untreatable. As a result, there has been a proliferation of scientific articles applying deep learning to a plethora of different problems. The interest in deep learning in agriculture has been continuously growing since the inception of this type of technique in the early 2010s. Soybeans, being one of the most important agricultural commodities, has frequently been the target of efforts in this regard. In this context, it can be challenging to keep track of a constantly evolving state of the art. This review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis of the main challenges and research gaps that still remain. The ultimate goal is to facilitate the leap from academic research to technologies that actually work under the difficult conditions found in the the field. Full article
(This article belongs to the Special Issue Application of Imaging and Artificial Intelligence in Seed Research)
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