Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Seed Structure and Early Development of the Sporophyte

Seed Structure and Early Development of the Sporophyte

Seed structures are composed of a protective seed coat surrounding a plant embryo, and a food store for the developing embryo. The embryo contains the precursor tissues for leaves, stem, and roots. The endosperm and cotyledons—seed leaves—act as the food reserves for the growing embryo.

Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cheminformatics and artificial intelligence for accelerating agrochemical discovery.

Frontiers in chemistry·2023

Same author

Information Theory in Computational Biology: Where We Stand Today.

Entropy (Basel, Switzerland)·2020

Same author

An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans.

Diabetes·2017

Same author

Systems biology-based identification of Mycobacterium tuberculosis persistence genes in mouse lungs.

mBio·2014

Same author

Fast association tests for genes with FAST.

PloS one·2013

Same author

HapZipper: sharing HapMap populations just got easier.

Nucleic acids research·2012

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026

Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 3, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

DeepSort: deep convolutional networks for sorting haploid maize seeds.

Balaji Veeramani¹, John W Raymond², Pritam Chanda²

¹Dow AgroSciences LLC, 9330 Zionsville Rd, Indianapolis, 46268, IN, USA. BVeeramani@dow.com.

BMC Bioinformatics

|October 28, 2018

Summary

This summary is machine-generated.

DeepSort, a deep convolutional network, accurately sorts haploid maize seeds, improving agricultural efficiency. This advanced computer vision method overcomes traditional challenges in distinguishing seeds for faster development of superior crop varieties.

Keywords:

Agriculture Convolutional neural networks Corn Deep learning Double haploid induction Molecular markers

More Related Videos

Lateral Root Inducible System in Arabidopsis and Maize

Lateral Root Inducible System in Arabidopsis and Maize

Published on: January 14, 2016

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Feb 3, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Lateral Root Inducible System in Arabidopsis and Maize

Lateral Root Inducible System in Arabidopsis and Maize

Published on: January 14, 2016

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Maize is a vital global crop, with the double haploid technique accelerating the development of superior seed varieties.
Traditional methods for distinguishing haploid maize seeds rely on manual visual inspection of molecular markers, which is inefficient for large-scale production.
Variability in marker expression and maize genotypes presents challenges for automated seed discrimination.

Purpose of the Study:

To develop a computer vision method for automated discrimination of haploid maize seeds.
To address the challenges of phenotypic variability and genotype heterogeneity in maize seed sorting.
To improve the efficiency and scalability of inbred maize line production.

Main Methods:

Application of a deep convolutional network (DeepSort) for automated seed sorting.
Utilizing convolutional layer activations to derive discriminating features for embryo regions.
Experimentation with different network architectures to evaluate performance based on layer depth.

Main Results:

The proposed DeepSort approach demonstrates superior performance compared to existing machine learning classifiers.
The network successfully derives features for discriminating embryo regions, outperforming methods based on color, texture, and morphology.
Performance is shown to be dependent on the depth of the convolutional network layers.

Conclusions:

DeepSort provides a robust solution for sorting haploid maize seeds, resilient to variations in phenotypic expression, seed shape, and embryo pose.
Deep learning and convolutional networks are poised to significantly advance research and product development in digital agriculture.
The developed method enhances the potential for large-scale production of inbred maize lines.