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Plants
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Computer Vision Techniques for Plant Phenomics Applications
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Computer Vision Techniques for Plant Phenomics Applications

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (30 July 2026) | Viewed by 5104

Editors

Dr. Xijian Fan

E-Mail Website
Guest Editor
College of Information Science and Technology & Artifical Intellengce, Nanjing Forestry University, Nanjing 210037, China
Interests: computer vision; deep learning; forestry remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Weiliang Wen

E-Mail Website
Guest Editor
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: digital plant; plant phenotyping; 3D modelling; 3D reconstruction; visual computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant phenomics, the study of plant traits and their responses to environmental conditions, is critical for advancing agricultural productivity, sustainability, and food security. With the rapid development of computer vision technologies, there is a transformative opportunity to enhance the precision, scalability, and efficiency of phenotyping processes. Computer vision techniques, such as image segmentation, feature extraction, and deep learning, have shown great promise in analyzing plant traits from high-throughput imaging data, enabling non-invasive and real-time monitoring of plant growth, stress responses, and yield potential.

However, adapting computer vision to plant phenomics presents unique challenges, including handling diverse plant morphologies, varying environmental conditions, and integrating multi-modal data (e.g., RGB, hyperspectral, and 3D imaging). This Special Issue aims to bring together cutting-edge research that addresses these challenges, fostering interdisciplinary collaboration between the computer vision and plant science communities. We invite contributions covering a wide range of topics, including, but not limited to, the following:

  • Advanced Image Processing Techniques: Novel algorithms for plant segmentation, trait extraction, and morphological analysis.
  • Deep Learning for Phenotyping: Applications of convolutional neural networks (CNNs), transformers, and other deep learning models in plant trait identification and classification.
  • Multi-Modal Data Integration: Combining RGB, hyperspectral, thermal, or 3D imaging for comprehensive phenotyping.
  • High-Throughput Phenotyping Systems: Automated platforms for large-scale plant imaging and analysis.
  • Robustness to Environmental Variability: Methods to handle varying lighting, backgrounds, and field conditions in outdoor phenotyping.
  • Real-Time Monitoring and Analysis: Computer vision solutions for dynamic, in situ plant monitoring.
  • Applications in Precision Agriculture: Use cases in crop breeding, stress detection, yield prediction, and resource optimization.
  • Open-Source Tools and Datasets: Development of publicly available software and benchmark datasets for plant phenomics research.

Dr. Xijian Fan
Dr. Weiliang Wen
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants 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 2700 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

  • computer vision
  • deep learning
  • UAV remote sensing
  • multisource fusion
  • plant phenomics
  • phenotypic traits extraction

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

Published Papers (3 papers)

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Research

22 pages, 6072 KB  
Article
A Deep Learning Model for Chili Pepper Fruit Shape Classification Using DenseNet-121 and CBAM
by Zongjun Li, Yinghua Li, Hu Zhao, Liping Huang, Zengjing Zhao, Jianjie Liao, Meng Wang, Xing Wu, Mingxia Gong, Zhi He, Liyan Liu and Risheng Wang
Plants 2026, 15(13), 2103; https://doi.org/10.3390/plants15132103 - 7 Jul 2026
Viewed by 578
Abstract
Traditional manual grading of fresh chili peppers suffers from inconsistent quality control and low efficiency. To meet the demand for accurate fruit shape recognition during the post-harvest stage, this study proposes an intelligent recognition method based on an improved DenseNet-121 network. This approach [...] Read more.
Traditional manual grading of fresh chili peppers suffers from inconsistent quality control and low efficiency. To meet the demand for accurate fruit shape recognition during the post-harvest stage, this study proposes an intelligent recognition method based on an improved DenseNet-121 network. This approach facilitates the application of machine vision in agricultural sorting equipment. DenseNet-121 serves as the backbone network. The Convolutional Block Attention Module (CBAM) is introduced to enhance feature focus on fruit shapes. A regularization strategy (Dropout = 0.3, weight decay = 1 × 10−4) and a cross-entropy loss function with label smoothing (LS = 0.1) are integrated to optimize decision boundaries. These configurations prevent the model from overfitting to hard training labels and yield a robust classification architecture. Experimental results demonstrate that the proposed model achieves a precision of 90.09%, a recall of 89.60%, an F1-score (the harmonic mean of precision and recall) of 89.53%, and an overall accuracy of 89.74%. The model contains 7.09 M parameters and requires a single-frame inference time of 7.35 ms. Comprehensive evaluations indicate that the proposed model achieves an optimal balance among environmental noise robustness, prediction accuracy, and computational efficiency. Consequently, by maintaining high fine-grained classification accuracy alongside a low memory footprint and rapid inference speed, the model demonstrates strong potential for real-time deployment on resource-constrained edge devices within actual agricultural optical sorting equipment. Full article
(This article belongs to the Special Issue Computer Vision Techniques for Plant Phenomics Applications)
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28 pages, 2738 KB  
Article
BCAR-Net: A Bidirectional Cross-Attention Network with Auxiliary Reconstruction for Tree Counting in Complex Forest Scenes Using Airborne RGB and LiDAR Data
by Xiaoyu Wu, Xijian Fan, Mengjiao Tang and Size Dai
Plants 2026, 15(12), 1762; https://doi.org/10.3390/plants15121762 - 6 Jun 2026
Viewed by 988
Abstract
Accurate tree counting from remote sensing data is essential for forest inventory, biomass estimation, carbon accounting, and ecological monitoring. However, existing approaches predominantly rely on airborne RGB imagery and often struggle in complex forest scenes where neighboring crowns exhibit highly similar textures and [...] Read more.
Accurate tree counting from remote sensing data is essential for forest inventory, biomass estimation, carbon accounting, and ecological monitoring. However, existing approaches predominantly rely on airborne RGB imagery and often struggle in complex forest scenes where neighboring crowns exhibit highly similar textures and colors and where overlapping crown boundaries become ambiguous. To address this limitation, the LiDAR-derived Canopy Height Model (CHM) is introduced as a complementary modality that provides explicit cues on canopy height variation and vertical structure to support RGB-based analysis. Building on this, we propose BCAR-Net, a broker-guided RGB and depth (RGB-D) multimodal framework that couples bidirectional cross-modal interaction, adaptive tri-branch fusion, and auxiliary reconstruction within a two-stage optimization scheme. Specifically, a bidirectional cross-attention U-Net generates an intermediate broker RGB-D representation from paired RGB images and depth maps through symmetric bidirectional cross-attention between the two modalities and direction-aware gating. The original RGB image, depth map, and broker representation are then jointly encoded by three weight-sharing branches and adaptively aggregated by a spatial fusion gate for density-map regression. To regularize the fused latent feature, a multi-scale cross-attention reconstruction decoder provides auxiliary RGB and depth reconstruction supervision by querying multi-scale BCA-UNet encoder features through 2D cross-attention, and a reconstruction-oriented first stage replaces externally generated fused-image supervision, yielding a task-consistent optimization scheme. Experiments on the NEONTreeEvaluation benchmark show that BCAR-Net consistently outperforms single-modality settings and direct RGB-D concatenation multimodal baseline. Additional experiments on a public UAV RGB–LiDAR dataset provide a small-scale supplementary evaluation under a different acquisition setting, where BCAR-Net achieves modest but consistent improvements over RGB-only and depth-only baselines. These results demonstrate that the proposed framework offers an effective but computationally cautious solution for tree counting in complex forest environments. Full article
(This article belongs to the Special Issue Computer Vision Techniques for Plant Phenomics Applications)
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17 pages, 3498 KB  
Article
Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China
by Muhammad Murtaza Zaka, Alim Samat, Jilili Abuduwaili, Enzhao Zhu, Arslan Akhtar and Wenbo Li
Plants 2025, 14(20), 3153; https://doi.org/10.3390/plants14203153 - 13 Oct 2025
Cited by 1 | Viewed by 2448
Abstract
Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early [...] Read more.
Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management. Full article
(This article belongs to the Special Issue Computer Vision Techniques for Plant Phenomics Applications)
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