From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection.

Chrisbin James, Yanyang Gu, Andries Potgieter, Etienne David, Simon Madec, Wei Guo, Frédéric Baret, Anders Eriksson, Scott Chapman

January 2023 Plant Phenomics

Synopsis of Social media discussions

The opinions demonstrate a positive outlook, highlighting how the pipeline automates manual counting, handles environmental variability, and can be adapted to different crops, showing both technical interest and recognition of its potential impact. Words like 'revolutionizing', 'robust', and 'scalable' reflect excitement and acknowledgment of the research's importance.

A
Agreement
 Moderate agreement

Most discussions express strong support and appreciation for the pipeline’s potential to improve crop yield estimation and address common challenges like domain shift.

I
Interest
 High level of interest

The enthusiasm for using deep learning in agriculture and its scalability indicates high interest among participants.

E
Engagement
 High engagement

Several discussions reference specific features such as automation, robustness, and applicability to other crops, showing deep engagement with the content.

I
Impact
 High level of impact

Participants believe that this pipeline could significantly transform agricultural practices, with mentions of improved accuracy and efficiency emphasizing its high impact.

Social Mentions

YouTube

1 Videos

Twitter

18 Posts

News

3 Articles

Metrics

Video Views

8

Total Likes

39

Extended Reach

50,749

Social Features

22

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Deep Learning Pipeline for Sorghum Panicle Detection and Density Estimation

Deep Learning Pipeline for Sorghum Panicle Detection and Density Estimation

This video presents a comprehensive process from image collection to deploying a deep learning model for automatic sorghum panicle counting and density mapping. It emphasizes creating robust models adaptable to diverse environmental conditions for improved crop management.

Social RTCL TV

October 1, 2023

8 views

  • Plant Phenomics
    @PPhenomics (Twitter)

    New study offers a deep-learning pipeline for sorghum panicle density estimation, improving crop yield analysis. It’s efficient, scalable, and adaptable to other grains. #Agritech #CropYield #MachineLearning Details: https://t.co/XzamePiS7K https://t.co/Az2Qj0EoG4
    view full post

    October 9, 2025

    2

  • Plant Phenomics
    @PPhenomics (Twitter)

    New study: We developed a deep-learning pipeline for sorghum panicle density estimation using RGB images. It offers a robust solution for accurate yield prediction, overcoming domain shift challenges. #MachineLearning #CropYield Details: https://t.co/XzamePiS7K https://t.co/JwMwuvk2PH
    view full post

    June 7, 2025

  • Plant Phenomics
    @PPhenomics (Twitter)

    We developed a comprehensive pipeline for deep-learning-assisted panicle yield estimation in sorghum, from data collection to model deployment. It addresses domain shifts and can be generalized to other grains. #MachineLearning #Sorghum Details: https://t.co/XzamePiS7K https://t.co/HDCzOPypxI
    view full post

    June 5, 2025

    4

  • Yu_Tanaka
    @YuTanaka6400 (Twitter)

    RT @PPhenomics: We've developed a deep-learning pipeline for sorghum panicle yield estimation using RGB images. It covers data collection t…
    view full post

    March 8, 2025

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    We've developed a deep-learning pipeline for sorghum panicle yield estimation using RGB images. It covers data collection to model deployment, addressing domain shift for robustness. #MachineLearning #CropYield Details: https://t.co/Dz1kYPxTDH https://t.co/lFC2v11v45
    view full post

    March 8, 2025

    6

    1


  • @latransgenica (Twitter)

    RT @PPhenomics: Study unveils deep-learning pipeline for sorghum panicle yield estimation, overcoming manual counting inefficiencies. Offer…
    view full post

    December 19, 2024

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    Study unveils deep-learning pipeline for sorghum panicle yield estimation, overcoming manual counting inefficiencies. Offers robust model to handle domain shift, applicable to other crops. #AgricultureTech #DeepLearning Details: https://t.co/Dz1kYPxTDH https://t.co/3DaNAQR2yS
    view full post

    December 19, 2024

    3

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    Introducing a deep-learning pipeline for sorghum yield estimation via head density mapping, overcoming manual counting inefficiencies and domain shift challenges in model deployment. Details: https://t.co/Dz1kYPxTDH https://t.co/uyZPhL13Ht
    view full post

    September 23, 2024

    6

  • Plant Phenomics
    @PPhenomics (Twitter)

    Introducing a deep-learning pipeline for panicle density estimation in sorghum, enhancing accuracy and efficiency in crop yield analysis. #AgricultureTech #MachineLearning #CropYield Details: https://t.co/Dz1kYPxTDH https://t.co/qKvN4lQbFm
    view full post

    July 26, 2024

    1

  • Jinliang Yang
    @JinliangYang (Twitter)

    RT @PPhenomics: New deep-learning pipeline for sorghum panicle yield estimation. Automates manual counting, adapts to domain shift, general…
    view full post

    June 10, 2024

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    New deep-learning pipeline for sorghum panicle yield estimation. Automates manual counting, adapts to domain shift, generalizes to other crops. #AgricultureTech #MachineLearning Details: https://t.co/Dz1kYPxlO9 https://t.co/tZh2KCPKbm
    view full post

    June 9, 2024

    8

    1

  • Ricardo Vázquez
    @ricardo_ik_ahau (Twitter)

    RT @PPhenomics: Revolutionizing crop yield estimation: Our pipeline integrates deep learning for accurate panicle density mapping in sorghu…
    view full post

    March 28, 2024

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    Revolutionizing crop yield estimation: Our pipeline integrates deep learning for accurate panicle density mapping in sorghum fields, paving the way for efficient agronomy scouting. #CropScience #DeepLearning Details:https://t.co/dwVhN9hJjw https://t.co/OQjBodUrLz
    view full post

    March 28, 2024

    1

    1

  • Nacer.louahdi
    @LouahdiNacer (Twitter)

    RT @PPhenomics: Panicle density crucial for crop yield estimation. Manual counting inefficient; machine learning aids, yet challenges remai…
    view full post

    March 7, 2024

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    Panicle density crucial for crop yield estimation. Manual counting inefficient; machine learning aids, yet challenges remain. Our pipeline offers deep-learning-assisted panicle yield estimation for sorghum, applicable across grain species. Details:https://t.co/Dz1kYPxTDH https://t.co/8ifyPJX6eB
    view full post

    March 6, 2024

    1

    1

  • TranSpread
    @transpread (Twitter)

    RT @PPhenomics: Our study provides a comprehensive pipeline for using deep learning in sorghum yield estimation, overcoming domain shift is…
    view full post

    June 11, 2023

    1

  • Plant Phenomics
    @PPhenomics (Twitter)

    Our study provides a comprehensive pipeline for using deep learning in sorghum yield estimation, overcoming domain shift issues. This can be applied to other grain species. #Agritech Details: https://t.co/Dz1kYPxTDH https://t.co/hlUXn2R31e
    view full post

    June 10, 2023

    6

    1

  • Phys.org Biology
    @physorg_biology (Twitter)

    Counting heads: How deep learning can simplify tedious agricultural tasks https://t.co/VBgJ26XavQ https://t.co/8VjK9ijBAs
    view full post

    March 7, 2023

    1

Abstract Synopsis

  • The paper develops a comprehensive process that takes you from collecting images of sorghum fields to training and deploying a deep learning model to automatically count panicles, which are important for predicting crop yield.
  • The authors emphasize the importance of creating a robust model that can handle different environmental conditions, as models trained in one setting might not perform well in others due to changes in natural environments.
  • Although demonstrated on sorghum, this pipeline can be adapted to other grain crops and offers a detailed, software-free approach to generating high-resolution density maps for better crop management.]