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Agronomy
Special Issues
Novel Studies in High-Throughput Plant Phenomics
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Novel Studies in High-Throughput Plant Phenomics

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 1463

Special Issue Editors

Dr. Ruifang Zhai

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: crop phenomics and computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Feng

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: CT; plant phenomics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wanneng Yang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Huazhong Agricultural University, Wuhan 430070, China
Interests: plant phenomics; high-throughput phenotyping; agircultural photonics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant phenomics is the scientific study of the comprehensive set of phenotypic traits exhibited by plants under various environmental conditions and their dynamic changes. It encompasses phenotype information at the molecular, physiological, and morphological levels. In recent decades, various technological innovations have been integrated to achieve comprehensive breakthroughs in crop phenomics. The transition from above-ground to below-ground, indoor to outdoor, physical to physiological, and macro to micro phenotyping highlights the multi-dimensional progress in this field. Therefore, this Special Issue seeks to highlight the significant strides in these frontier areas, showcasing the latest advancements, methodologies, and applications focusing on the technologies, methods, and applications that are aiding in our deeper understanding of crop sciences.

Given the above context, this Special Issue invites submissions broadly contributing to plant phenomics. Specific topics of interest include the following:

  • Non-destructive observation and trait extraction of crop root systems;
  • High-throughput, dynamic, and non-destructive extraction of field crop phenotypes;
  • High-throughput extraction techniques for physiological phenotypes;
  • High-throughput phenotyping techniques for crop microscopic traits;
  • Novel sensor technologies in crop phenotyping.

Dr. Ruifang Zhai
Dr. Hui Feng
Prof. Dr. Wanneng Yang
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. Agronomy 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 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

  • root phenotyping
  • micro phenotyping
  • field phenotyping
  • physiological phenotyping
  • novel sensing technology
  • deep learning

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

Published Papers (2 papers)

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Research

31 pages, 3603 KB  
Article
High-Throughput Citrus Detection via Citrus-SGYOLOv2: A Symmetric Ghost-Based Architecture with High-Resolution Feature Fusion
by Jinfeng Li, Yutian Miao, Wenxuan Guo, Yuxiang Li, Qian Xu, Yue Xiang, Yanyu Chen, Xianyao Wang, Yunsen Liang and Jun Li
Agronomy 2026, 16(9), 894; https://doi.org/10.3390/agronomy16090894 - 28 Apr 2026
Viewed by 216
Abstract
Accurate high-throughput fruit detection is the core prerequisite for precision citrus management. Existing models face a critical trade-off between accuracy for small fruits and computational efficiency, restricting large-scale industry transformation. To resolve this, we propose Citrus-SGYOLOv2, an optimized deep learning architecture specifically engineered [...] Read more.
Accurate high-throughput fruit detection is the core prerequisite for precision citrus management. Existing models face a critical trade-off between accuracy for small fruits and computational efficiency, restricting large-scale industry transformation. To resolve this, we propose Citrus-SGYOLOv2, an optimized deep learning architecture specifically engineered for high-throughput phenotypic monitoring. The primary contribution of this work lies in three synergistic innovations: a novel Symmetric Ghost Backbone that prunes architectural redundancy while maintaining hierarchical feature depth; a Citrus Color Prior Calibration Attention Mechanism (Citrus_SE) that embeds physiological chromaticity priors to suppress complex spectral noise from foliage; and a P2-layer-based full-scale fusion strategy designed to recover fine-grained spatial details lost during downsampling. Experiments on our self-built dataset show that Citrus-SGYOLOv2 achieves 95.54% mAP@50 and 77.13% mAP@50–95, outperforming YOLOv11s by 5.03 and 9.90 percentage points respectively. Notably, the model achieves a 48.8% reduction in parameters (4.84 M) while sustaining a high-throughput inference speed of 139.00 FPS. This research provides a robust and efficient foundational framework for intelligent yield estimation and precision orchard management. Full article
(This article belongs to the Special Issue Novel Studies in High-Throughput Plant Phenomics)
28 pages, 11495 KB  
Article
A Pipeline for Mushroom Mass Estimation Based on Phenotypic Parameters: A Multiple Oudemansiella raphanipies Model
by Hua Yin, Danying Lei, Anping Xiong, Lu Yuan, Minghui Chen, Yilu Xu, Yinglong Wang, Hui Xiao and Quan Wei
Agronomy 2026, 16(1), 124; https://doi.org/10.3390/agronomy16010124 - 4 Jan 2026
Viewed by 453
Abstract
Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable in optimizing post-harvest packaging processes. Traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in [...] Read more.
Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable in optimizing post-harvest packaging processes. Traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in this work, we developed a novel pipeline for estimating the mass of multiple Oudemansiella raphanipies. To achieve this goal, an enhanced deep learning (DL) algorithm for instance segmentation and a machine learning (ML) model for mass prediction were introduced. On one hand, to segment multiple samples in the same image, a novel instance segmentation network named FinePoint-ORSeg was applied to obtain the finer edges of samples, by integrating an edge attention module to improve the fineness of the edges. On the other hand, for individual samples, a novel cap–stem segmentation approach was applied and 18 phenotypic parameters were obtained. Furthermore, principal component analysis (PCA) was utilized to reduce the redundancy among features. Combining the two aspects mentioned above, the mass was computed by an exponential GPR model with seven principal components. In terms of segmentation performance, our model outperforms the original Mask R-CNN; the AP, AP50, AP75, and APs are improved by 2%, 0.7%, 1.9%, and 0.3%, respectively. Additionally, our model outperforms other networks such as YOLACT, SOLOV2, and Mask R-CNN with Swin. As for mass estimation, the results show that the average coefficient of variation (CV) of a single sample mass in different attitudes is 6.81%. Moreover, the average mean absolute percentage error (MAPE) for multiple samples is 8.53%. Overall, the experimental results indicate that the proposed method is time-saving, non-destructive, and accurate. This can provide a reference for research on post-harvest packaging technology for Oudemansiella raphanipies. Full article
(This article belongs to the Special Issue Novel Studies in High-Throughput Plant Phenomics)
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