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Advances in Artificial Intelligence for Plant Research—2nd Edition
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Advances in Artificial Intelligence for Plant Research—2nd Edition

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

Deadline for manuscript submissions: 30 December 2026 | Viewed by 4414

Special Issue Editors

Dr. Guoxiong Zhou

E-Mail Website
Guest Editor
College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
Interests: artificial intelligence; computer vision; plant phenotyping; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Liujun Li

E-Mail Website
Guest Editor
Agricultural and Life Sciences, Department of Soil and Water SystemsUniversity of Idaho, Moscow, ID, USA
Interests: robotics sensing; decision support systems; climate-smart agriculture; precision agriculture; intelligent robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaoyulong Chen

E-Mail Website
Guest Editor
Key Laboratory of Karst Geological Resources and Environment, Guizhou University, Guiyang 550025, China
Interests: pest management; biocontrol; smart agriculture; deep learning; image recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advances in artificial intelligence offer a transformative solution for botanical research that promises to revolutionize crop management, disease prediction, precision agriculture, and sustainable ecosystem management. This topic will focus on the latest advances, challenges, and opportunities in artificial intelligence in the field of plant research, promoting interdisciplinary collaboration and driving significant advances in plant science. Specific research topics include, but are not limited to, the following:

  1. Plant phenotype analysis: The application of computer vision and machine learning technology to identify and analyze the morphological characteristics and growth state of plants.
  2. Plant disease detection and prediction: Using AI technology to predict and identify plant diseases to improve early warning and management efficiency.
  3. Crop management and optimization: Combining data analytics and AI algorithms to optimize fertilization, irrigation, and other agricultural practices to improve crop yield and quality.
  4. Plant genomics and genetic research: Using AI-assisted genome analysis and genetic algorithms to accelerate plant genetic improvement and new variety development.
  5. Environmental monitoring and adaptation: Using AI to monitor the impact of environmental factors on plant growth and help develop plant varieties that adapt to different climatic conditions.
  6. Agricultural robots and automation: Studying the application of AI-driven agricultural robots in seeding, picking, and weed control to improve the efficiency of agricultural operations.

Dr. Guoxiong Zhou
Dr. Liujun Li
Prof. Dr. Xiaoyulong Chen
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. 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

  • plant research
  • artificial intelligence
  • phenotype analysis
  • disease detection
  • crop management
  • optimization agricultural robots

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Published Papers (5 papers)

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28 pages, 9414 KB  
Article
FCDNet: An Efficient and Cost-Effective Strawberry Disease Detection Model for Smart Farming Management
by Ruoyu Ouyang, Junying Jiang, Yujia Shao, Jialei Zhan and Xiaoyu Zhang
Plants 2026, 15(9), 1341; https://doi.org/10.3390/plants15091341 - 28 Apr 2026
Viewed by 220
Abstract
With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong [...] Read more.
With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong background interference, multiple concurrent diseases, and fine-grained lesion differences, posing significant challenges to existing detection methods in practical agricultural Internet of Things (IoT) applications. In this paper, we propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition in complex field management scenarios. The proposed model integrates a Freq-Spatial Feature Module (FSFM), a Context Guide Fusion Module (CGFM), and a Task Align Dynamic Detection Head (TADDH), enabling enhanced expression of high-frequency micro-lesions, adaptive filtering of field background noise, and spatial alignment of classification and regression tasks, while maintaining a lightweight architecture suitable for low-cost agricultural edge devices. Extensive experiments conducted on the newly constructed Strawberry Disease Dataset-7(S7DD) demonstrate that FCDNet consistently outperforms existing mainstream methods, achieving an F1-score of 91.0% and an mAP@0.5 of 94.6%. The model’s architectural robustness and capacity for generalization are further substantiated by evaluations across diverse agricultural datasets using PlantDoc and ALDOD. Ultimately, FCDNet became a practical and cost-effective tool for real-time detection of strawberry diseases, directly supporting more accurate yield forecasting and risk management in smart agriculture systems. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)
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30 pages, 3658 KB  
Article
TB-DLossNet: Fine-Grained Segmentation of Tea Leaf Diseases Based on Semantic-Visual Fusion
by Shuqi Zheng, Hao Zhou, Ziyang Shi, Fulin Su, Wei Shi, Ruifeng Liu, Lin Li and Fangying Wan
Plants 2026, 15(7), 1035; https://doi.org/10.3390/plants15071035 - 27 Mar 2026
Viewed by 561
Abstract
Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with [...] Read more.
Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with three primary challenges: semantic ambiguity arising from evolving pathological stages, blurred boundaries due to overlapping lesions, and the high omission rate of micro-lesions. To address these issues, this paper presents TB-DLossNet (Text-Conditioned Boundary-Aware Network with Dynamic Loss Reweighting), a novel segmentation framework based on semantic-visual multi-modal fusion. Leveraging VMamba as the visual backbone, the proposed model innovatively integrates BERT-encoded structured text as an auxiliary modality to resolve visual ambiguities through cross-modal semantic guidance. Furthermore, a boundary enhancement branch is incorporated alongside a multi-scale deep supervision strategy to mitigate boundary displacement and ensure the topological continuity of lesion structures. To tackle the detection of small-scale targets, we designed a dynamic weight loss function conditioned on lesion area, significantly bolstering the model’s sensitivity to minute pathological features. Additionally, to alleviate the scarcity of high-quality data, we curated a comprehensive multi-modal dataset encompassing seven typical diseases of Camellia oleifera. Experimental results demonstrate that TB-DLossNet achieves a Mean Intersection over Union (mIoU) of 87.02%, outperforming the state-of-the-art unimodal VMamba and multimodal Lvit by 4.9% and 2.59%, respectively. Qualitative evaluations confirm that our model exhibits lower false-negative rates and superior boundary-fitting precision in heterogeneous field scenarios. Finally, generalization tests on an apple disease dataset further validate the robustness and transferability of the proposed framework. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)
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38 pages, 5694 KB  
Article
Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses
by Rubi Quiñones, Francisco Muñoz-Arriola, Sruti Das Choudhury and Ashok Samal
Plants 2026, 15(3), 428; https://doi.org/10.3390/plants15030428 - 30 Jan 2026
Viewed by 1070
Abstract
Advancements in phenotyping technologies, including object imaging, high-throughput monitoring, and soft computing, are pivotal for understanding plant responses to environmental stresses. These technologies enable detailed analyses of morphological, physiological, and structural adaptations under abiotic and biotic stresses, such as drought. Current work using [...] Read more.
Advancements in phenotyping technologies, including object imaging, high-throughput monitoring, and soft computing, are pivotal for understanding plant responses to environmental stresses. These technologies enable detailed analyses of morphological, physiological, and structural adaptations under abiotic and biotic stresses, such as drought. Current work using multimodal and multi-perspective image processing methods can capture the essential processes that enhance plant resilience and counteract stress by identifying morphological and biochemical indicators. However, the dynamic and complex nature of plant responses poses multiple challenges for generating precise analytics and descriptors of evolving phenotypes. This work introduces analytics for concurrent imaging, adopting the underlying principle of cosegmentation to create taxonomies for new phenotypes. Here, unidimensional refers to the concurrent analysis of multiple images within a single phenotyping dimension: temporal, modal, or perspective, rather than combining information across dimensions. The proposed unidimensional phenotypes integrate concurrent images within individual temporal, modal, or perspective dimensions to capture dynamic morphological and physiological responses that are not observable with conventional single-image or cumulative metrics. Within a high-throughput imagery production system, these phenotypes enable more nuanced quantification of phenotypic changes, leveraging the strengths of simultaneous image analysis to enhance insight into plant adaptations. This workflow aligns with the investigation of plants’ adaptive strategies under abiotic stress and provides quantitative indicators of plant health under adverse environmental conditions. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)
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21 pages, 6740 KB  
Article
Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies
by Junxiang Tan, Hao Chen, Kaihui Zhang, Hao Yang, Xiongjie Wang, Ronghao Yang, Guyue Hu, Shaoda Li, Jianfei Liu and Xiangjun Wang
Plants 2026, 15(3), 376; https://doi.org/10.3390/plants15030376 - 26 Jan 2026
Viewed by 727
Abstract
Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that [...] Read more.
Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems. The proposed approach begins by extracting stable wooden structures through a region-of-interest (ROI) segmentation process. Repeatable WSKs are then generated using a newly proposed wood structure significance (WSS) score, which quantifies and identifies salient regions across multi-view data. For transformation estimation, descriptor matching, WSS constraints, and geometric consistency optimization are integrated into a fast global registration (FGR) pipeline. Extensive evaluation across 25 plots covering 5 sites at the National rubber plantation base in Danzhou, Hainan, China, demonstrates that the method achieves a mean co-registration accuracy of 9 cm. Further analysis under varying seasonal canopy complexities confirms its robustness and critical role in enabling high-precision rubber tree phenotyping. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)
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16 pages, 2697 KB  
Article
Real-Time Callus Instance Segmentation in Plant Tissue Culture Using Successive Generations of YOLO Architectures
by Yunus Egi, Tülay Oter, Mortaza Hajyzadeh and Muammer Catak
Plants 2026, 15(1), 47; https://doi.org/10.3390/plants15010047 - 23 Dec 2025
Viewed by 1060
Abstract
Callus induction is a complex procedure in plant organ, cell, and tissue culture that underpins processes such as metabolite production, regeneration, and genetic transformation. It is important to monitor callus formation alongside subjective evaluations, which require labor-intensive care. In this research, the first [...] Read more.
Callus induction is a complex procedure in plant organ, cell, and tissue culture that underpins processes such as metabolite production, regeneration, and genetic transformation. It is important to monitor callus formation alongside subjective evaluations, which require labor-intensive care. In this research, the first curated lentil (Lens culinaris) callus dataset for instance segmentation was experimentally generated using three genotypes as one data set: Firat-87, Cagil, and Tigris. Leaf explants were cultured on MS medium fortified with different concentrations of gross regulators of BA and NAA to induce callus formation. Three biologically relevant stages, the leaf stage, the green callus, and the necrosis callus, were produced. During this process, 122 high-resolution images were obtained, resulting in 1185 total annotations across them. The dataset was evaluated across four successive generations (v5/7/8/11) of YOLO deep learning models under identical conditions using mAP, Dice coefficient, Precision, Recall, and IoU, together with efficiency metrics including parameter counts, FLOPs, and inference speed. The results show that anchor-based variants (YOLOv5/7) relied on predefined priors and showed limited boundary precision, whereas anchor-free designs (YOLOv8/11) used decoupled heads and direct center/boundary regression that provided clear advantages for callus structures. YOLOv8 reached the highest instance segmentation precision with mAP50@0.855, while it matched the accuracy with greater efficiency and achieved real-time inference with 166 FPS. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)
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