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Seeds
Special Issues
Agrotechnics in Seed Quality: Current Progress and Challenges
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Agrotechnics in Seed Quality: Current Progress and Challenges

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

Deadline for manuscript submissions: closed (25 September 2025) | Viewed by 1014

Special Issue Editors

Dr. Edvaldo Aparecido Amaral Da Silva

E-Mail Website
Guest Editor
Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu 18610-034, SP, Brazil
Interests: seed physiology; molecular biology; imaging technology
Special Issues, Collections and Topics in MDPI journals
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

Special Issue Information

Dear Colleagues,

Around the world, the seed industry has undergone many transformations driven by recent trends in digital agriculture, such as robust optical sensors, software, robotics, automation, and sophisticated data analyses. Sustainable methods have been developed based on non-destructive measurements without relying on human vision.

An important approach that has contributed to the implementation of digital solutions is the creation of machine learning models. These models can automatically diagnose the genetic, physical, chemical, physiological, and health attributes of seed quality. For example, knowledge of the electromagnetic properties of the seed tissues has enabled the non-invasive detection of mechanical damage, insects, and physiological disturbances in agricultural seeds.

This Special Issue focuses on the main technologies for autonomous seed quality screening, including spectroscopy, multispectral imaging, radiographs, and autofluorescence, among others, with an emphasis on agricultural challenges and current trends to assess seed quality parameters.

You may choose our Joint Special Issue in Agronomy.

Dr. Edvaldo Aparecido Amaral Da Silva
Dr. Clíssia Barboza Mastrangelo
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 250 words) can be sent to the Editorial Office for assessment.

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 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 1200 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

  • multispectral imaging
  • spectroscopy
  • fluorescence
  • radiography
  • hyperspectral image
  • tomography
  • magnetic resonance
  • machine learning
  • seed vigor
  • fungi

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Published Papers (1 paper)

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Research

23 pages, 7244 KB  
Article
Computer Vision for Cover Crop Seed-Mix Detection and Quantification
by Karishma Kumari, Kwanghee Won and Ali M. Nafchi
Seeds 2025, 4(4), 59; https://doi.org/10.3390/seeds4040059 - 12 Nov 2025
Viewed by 303
Abstract
Cover crop mixes play an important role in enhancing soil health, nutrient turnover, and ecosystem resilience; yet, maintaining even seed dispersion and planting uniformity is difficult due to significant variances in seed physical and aerodynamic properties. These discrepancies produce non-uniform seeding and species [...] Read more.
Cover crop mixes play an important role in enhancing soil health, nutrient turnover, and ecosystem resilience; yet, maintaining even seed dispersion and planting uniformity is difficult due to significant variances in seed physical and aerodynamic properties. These discrepancies produce non-uniform seeding and species separation in drill hoppers, which has an impact on stand establishment and biomass stability. The thousand-grain weight is an important measure for determining cover crop seed quality and yield since it represents the weight of 1000 seeds in grams. Accurate seed counting is thus a key factor in calculating thousand-grain weight. Accurate mixed-seed identification is also helpful in breeding, phenotypic assessment, and the detection of moldy or damaged grains. However, in real-world conditions, the overlap and thickness of adhesion of mixed seeds make precise counting difficult, necessitating current research into powerful seed detection. This study addresses these issues by integrating deep learning-based computer vision algorithms for multi-seed detection and counting in cover crop mixes. The Canon LP-E6N R6 5D Mark IV camera was used to capture high-resolution photos of flax, hairy vetch, red clover, radish, and rye seeds. The dataset was annotated, augmented, and preprocessed on RoboFlow, split into train, validation, and test splits. Two top models, YOLOv5 and YOLOv7, were tested for multi-seed detection accuracy. The results showed that YOLOv7 outperformed YOLOv5 with 98.5% accuracy, 98.7% recall, and a mean Average Precision (mAP 0–95) of 76.0%. The results show that deep learning-based models can accurately recognize and count mixed seeds using automated methods, which has practical applications in seed drill calibration, thousand-grain weight estimation, and fair cover crop establishment. Full article
(This article belongs to the Special Issue Agrotechnics in Seed Quality: Current Progress and Challenges)
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