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Agronomy
Special Issues
Novel Approaches to Phenotyping in Plant Research
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Novel Approaches to Phenotyping in Plant Research

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Plant-Crop Biology and Biochemistry".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 271

Special Issue Editor

Dr. Robert Jackson

E-Mail Website
Guest Editor
Data Sciences Department, Crop Science Centre, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
Interests: remote sensing; phenotyping; crops; agronomy; AI/machine learning

Special Issue Information

Dear Colleagues,

With the advancement of genotyping technologies at the turn of the century, phenotyping became a major bottleneck in plant and agricultural research. Even today, a significant amount of field-based research is performed by hand, which can be slow, expensive, and prone to human error. With the advancement of imaging technologies and, more recently, AI methodologies, plant phenotyping is becoming quicker, easier, and more accessible. With that in mind, we are initiating a Special Issue of Agronomy titled “Novel Approaches to Phenotyping in Plant Research”. This issue aims to highlight the exciting advancements that are being seen in plant phenotyping, from the lab, to the field, to even the county level, using tools ranging from low-cost self-made equipment for the lab to satellite data. We encourage submissions on the following topics:

  • Novel plant phenotyping technology;
  • Advancements in plant phenotyping methods;
  • The use of AI in phenotyping;
  • Multi-scale phenotyping.

Dr. Robert Jackson
Guest Editor

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 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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • plant phenotyping
  • multiscale phenotyping
  • novel methods
  • low-cost approaches
  • AI-driven phenotyping
  • field-based research
  • lab-based research
  • agriculture
  • horticulture

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

12 pages, 2695 KiB  
Article
End-to-End Deep Learning Approach to Automated Phenotyping of Greenhouse-Grown Plant Shoots
by Evgeny Gladilin, Narendra Narisetti, Kerstin Neumann and Thomas Altmann
Agronomy 2025, 15(5), 1117; https://doi.org/10.3390/agronomy15051117 - 30 Apr 2025
Viewed by 129
Abstract
High-throughput image analysis is a key tool for the efficient assessment of quantitative plant phenotypes. A typical approach to the computation of quantitative plant traits from image data consists of two major steps including (i) image segmentation followed by (ii) calculation of quantitative [...] Read more.
High-throughput image analysis is a key tool for the efficient assessment of quantitative plant phenotypes. A typical approach to the computation of quantitative plant traits from image data consists of two major steps including (i) image segmentation followed by (ii) calculation of quantitative traits of segmented plant structures. Despite substantial advancements in deep learning-based segmentation techniques, minor artifacts of image segmentation cannot be completely avoided. For several commonly used traits including plant width, height, convex hull, etc., even small inaccuracies in image segmentation can lead to large errors. Ad hoc approaches to cleaning ’small noisy structures’ are, in general, data-dependent and may lead to substantial loss of relevant small plant structures and, consequently, falsified phenotypic traits. Here, we present a straightforward end-to-end approach to direct computation of phenotypic traits from image data using a deep learning regression model. Our experimental results show that image-to-trait regression models outperform a conventional segmentation-based approach for a number of commonly sought plant traits of plant morphology and health including shoot area, linear dimensions and color fingerprints. Since segmentation is missing in predictions of regression models, visualization of activation layer maps can still be used as a blueprint to model explainability. Although end-to-end models have a number of limitations compared to more complex network architectures, they can still be of interest for multiple phenotyping scenarios with fixed optical setups (such as high-throughput greenhouse screenings), where the accuracy of routine trait predictions and not necessarily the generalizability is the primary goal. Full article
(This article belongs to the Special Issue Novel Approaches to Phenotyping in Plant Research)
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