Crop Agnostic Monitoring Driven by Deep Learning.

Michael Halstead, Alireza Ahmadi, Claus Smitt, Oliver Schmittmann, Chris McCool

December 2021 Front Plant Sci

Synopsis of Social media discussions

Discussions frequently praise the article's innovative approach to adaptable crop monitoring, with phrases like 'novel field agnostic monitoring technique' and references to its use on various robots and environments. The tone reflects appreciation for its practical implications and future impact, demonstrating a high level of engagement and recognition of its significance in modern agriculture.

A
Agreement
 Moderate agreement

Most discussions agree that the research offers valuable insights into flexible, drone-assisted crop monitoring, highlighting its potential to improve farm decision-making.

I
Interest
 High level of interest

Posts demonstrate high interest by emphasizing the innovative nature of field-agnostic monitoring and its relevance to modern farming challenges.

E
Engagement
 Moderate level of engagement

Commenters reference key concepts like 'deep learning,' 'crop management,' and 'autonomous systems,' indicating meaningful engagement with the technical content.

I
Impact
 Moderate level of impact

The mention of reducing physical labor and improving agronomic decisions suggests an acknowledgment of the potential transformative effect on the agricultural sector.

Social Mentions

YouTube

3 Videos

Twitter

4 Posts

LinkedIn

1 Posts

News

1 Articles

Metrics

Video Views

1,929

Total Likes

21

Extended Reach

27,226

Social Features

9

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

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  • Gabriel Schaaf
    @GabrielSchaaf (Twitter)

    RT @PhenoRob: Farmers require diverse & complex info to make agronomical decisions about crop management. Chris McCool presents a novel fie…
    view full post

    March 13, 2023

    1

  • PhenoRob
    @PhenoRob (Twitter)

    Farmers require diverse & complex info to make agronomical decisions about crop management. Chris McCool presents a novel field agnostic monitoring technique that is able to operate on 2 different robots, in arable farmland or a horticultural setting. https://t.co/OwZ8ur0h9Q
    view full post

    March 10, 2023

    7

    1

  • Australian Cotton Science
    @AusCottonSci (Twitter)

    Crop Agnostic Monitoring Driven by Deep Learning https://t.co/WNS8pcOk9L Agnostic monitoring algorithm leveraged computer vision, deep learning, and robotics to reduce physical monitoring of fields by farmers-IW @UniBonn https://t.co/ejSmdl9XLS
    view full post

    January 10, 2022

    1

  • Frontiers - Plant Science
    @FrontPlantSci (Twitter)

    New Research: Crop Agnostic Monitoring Driven by Deep Learning: Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is… https://t.co/aRYzBzHe3I #PlantScience #PlantSci
    view full post

    December 20, 2021

  • 1 s2.0 S0261219423003447 Main | PDF | Herbicide | Artificial ...

    2021.786702. review. Plant Methods 17, 22. https://doi.org/10.1186/s13007 ... 10.3389/fpls.2022.1053329. Soft Computing 27, 669–682. https://doi.org ...
    view full post

    December 12, 2025

    News

  • Chris McCool posted on LinkedIn

    doi: 10.3389/fpls.2021.786702 #video #science #robots #crops #monitoring #deeplearning · Crop ...
    view full post

    March 14, 2023

    LinkedIn

Abstract Synopsis

  • The text discusses a new deep learning-based monitoring system that is adaptable to different farming environments, such as open fields and glasshouses, helping farmers gather crucial crop and weed data more efficiently.
  • It uses instance segmentation to identify and locate crops and weeds at the species level, estimating crop ripeness and yield with high accuracy, and introduces a new matching criterion to improve fruit counting in glasshouses.
  • The approach significantly outperforms previous methods, especially in complex scenes, and provides actionable insights like intervention impacts to assist farmers in decision-making.]