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Plants
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AI-Driven Machine Vision Technologies in Plant Science
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AI-Driven Machine Vision Technologies in Plant Science

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 3345

Special Issue Editors

Dr. Dayang Liu

E-Mail Website
Guest Editor
College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Interests: machine learning; image processing; artificial intelligence; spectrum technology; food quality monitoring
Dr. Yi Shi

E-Mail Website
Guest Editor
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Interests: smart agriculture; machine learning; image processing; artificial intelligence

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) and machine vision technologies has revolutionized plant science, offering unprecedented opportunities for precision agriculture and sustainable forestry. These advanced tools enable automated plant phenotyping, disease detection, yield prediction, and environmental monitoring with high accuracy and efficiency. Machine learning algorithms, particularly deep learning, have enhanced image-based analysis of crops and forest ecosystems, while AI-driven decision support systems optimize resource management and reduce environmental impact. However, challenges remain in scalability, real-time processing, and model interpretability. This Special Issue seeks to compile cutting-edge research on AI and machine vision applications in plant science, highlighting innovations that bridge the gap between laboratory research and field deployment.

Scope of the Special Issue：

We invite original research articles, reviews, and case studies focusing on (but not limited to) the following topics:

  • AI and deep learning for plant phenotyping (morphological trait extraction, growth monitoring);
  • Machine vision-based disease and pest detection in crops and forests;
  • Yield prediction and quality assessment using hyper/multispectral imaging;
  • Robotics and automation for smart farming and precision forestry;
  • Explainable AI (XAI) and interpretable models for agri-forestry applications;
  • Edge AI and real-time vision systems for field deployment;
  • Datasets and benchmarking for AI in plant science.

This Special Issue aims to foster interdisciplinary collaboration among computer scientists, agronomists, and forestry experts, promoting scalable and sustainable solutions for modern plant science.

Dr. Dayang Liu
Dr. Yi Shi
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

  • AI and deep learning for plant phenotyping
  • machine vision-based disease and pest detection
  • yield prediction and quality assessment
  • robotics and automation
  • explainable AI (XAI) and interpretable models
  • edge AI and real-time vision systems
  • datasets and benchmarking

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

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Research

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31 pages, 8745 KB  
Article
Research on Tomato Quality Prediction Models Based on the Coupling of Environmental Factors and Appearance Phenotypes
by Longwei Liang, Zhaoyuan Wang, Kaige Liu, Jing Xu, Changhong Li, Huiying Liu and Ming Diao
Plants 2025, 14(23), 3569; https://doi.org/10.3390/plants14233569 - 22 Nov 2025
Viewed by 668
Abstract
This study addresses the limitations of current non-destructive techniques for assessing tomato quality, such as their high cost, strong dependence on spectroscopic instruments, and difficulty in dynamic monitoring. The study proposes an integrated tomato quality prediction model that combines a Long Short-Term Memory [...] Read more.
This study addresses the limitations of current non-destructive techniques for assessing tomato quality, such as their high cost, strong dependence on spectroscopic instruments, and difficulty in dynamic monitoring. The study proposes an integrated tomato quality prediction model that combines a Long Short-Term Memory (LSTM)-based environmental predictor, a Gated Recurrent Unit with attention mechanism (GRU-AT) for dynamic maturity prediction, and a Deep Neural Network (DNN)-based quality evaluation module. The LSTM model demonstrated high accuracy in environmental prediction (R2 > 0.9559). The GRU-AT model excelled in color ratio prediction (R2 > 0.86), and the DNN model achieved R2 values exceeding 0.811 for lycopene (LYC), firmness (FI), and soluble solids content (SSC). Experimental results demonstrate that this approach can accurately predict multiple quality parameters using only standard RGB images. In summary, this study provides a low-cost, low-complexity solution enabling real-time, non-destructive monitoring of greenhouse tomato quality, offering a viable pathway for crop quality management in precision agriculture. Full article
(This article belongs to the Special Issue AI-Driven Machine Vision Technologies in Plant Science)
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23 pages, 7270 KB  
Article
DHN-YOLO: A Joint Detection Algorithm for Strawberries at Different Maturity Stages and Key Harvesting Points
by Hongrui Hao, Juan Xi, Jingyuan Dai, Guozheng Wang, Dayang Liu and Liangkuan Zhu
Plants 2025, 14(22), 3439; https://doi.org/10.3390/plants14223439 - 10 Nov 2025
Viewed by 1001
Abstract
Strawberries are important cash crops. Traditional manual picking is costly and inefficient, while automated harvesting robots are hindered by field challenges like stem-leaf occlusion, fruit overlap, and appearance/maturity variations from lighting and viewing angles. To address the need for accurate cross-maturity fruit identification [...] Read more.
Strawberries are important cash crops. Traditional manual picking is costly and inefficient, while automated harvesting robots are hindered by field challenges like stem-leaf occlusion, fruit overlap, and appearance/maturity variations from lighting and viewing angles. To address the need for accurate cross-maturity fruit identification and keypoint detection, this study constructed a strawberry image dataset covering multiple varieties, ripening stages, and complex ridge-cultivation field conditions: MSRBerry. Based on the YOLO11-pose framework, we proposed DHN-YOLO with three key improvements: replacing the original C2PSA with the CDC module to enhance subtle feature capture and irregular shape adaptability; substituting C3K2 with C3H to strengthen multi-scale feature extraction and robustness to lighting-induced maturity/color variations; and upgrading the neck into a New-Neck via CA and dual-path fusion to reduce feature loss and improve critical region perception. These modifications enhanced feature quality while cutting parameters and accelerating inference. Experimental results showed DHN-YOLO achieved 87.3% precision, 88% recall, and 78.6% mAP@50:95 for strawberry detection (0.9%, 1.6%, 5% higher than YOLO11-pose), and 83%, 87.5%, 83.6% for keypoint detection (1.9%, 2.1%, 4.6% improvements). It also reached 71.6 FPS with 15 ms single-image inference. The overall performance of DHN-YOLO also surpasses other mainstream models such as YOLO13, YOLO10, DETR and so on. This demonstrates DHN-YOLO meets practical needs for robust strawberry and picking point detection in complex agricultural environments. Full article
(This article belongs to the Special Issue AI-Driven Machine Vision Technologies in Plant Science)
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22 pages, 10753 KB  
Article
Tomato Leaf Disease Detection Method Based on Multi-Scale Feature Fusion
by Xiangrui Meng, Cong Chen, Wenxue Dong and Ke Wang
Plants 2025, 14(20), 3174; https://doi.org/10.3390/plants14203174 - 16 Oct 2025
Viewed by 965
Abstract
Tomato is a key economic crop whose yield and quality depend heavily on the early and accurate detection of leaf diseases. Conventional diagnosis based on manual observation is labor-intensive and prone to subjective bias. To overcome the limitations of disease detection under complex [...] Read more.
Tomato is a key economic crop whose yield and quality depend heavily on the early and accurate detection of leaf diseases. Conventional diagnosis based on manual observation is labor-intensive and prone to subjective bias. To overcome the limitations of disease detection under complex environmental conditions, this study presents an enhanced YOLO11n-based detection framework for tomato leaf diseases. The proposed model integrates an EfficientMSF module in the backbone to strengthen multi-scale feature extraction, introduces a C2CU module to enhance global contextual representation, and employs a CAFMFusion module to achieve efficient fusion of local and global features. Experiments were conducted on a self-constructed dataset containing nine tomato leaf categories, including eight disease types and healthy samples. The proposed approach achieves an average Recall of 71.0%, mAP@0.5 of 76.5%, and mAP@0.5–0.95 of 60.5%, outperforming the baseline YOLO11n by 3.4%, 1.3%, and 2.0%, respectively. In particular, for the challenging Leaf Mold class, mAP@0.5 improved by 3.4%. These results demonstrate that the proposed method possesses strong robustness and practical applicability in complex field conditions, offering an effective solution for intelligent tomato disease monitoring and precision agricultural management. Full article
(This article belongs to the Special Issue AI-Driven Machine Vision Technologies in Plant Science)
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Other

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15 pages, 6022 KB  
Perspective
A Multidimensional Approach to Cereal Caryopsis Development: Insights into Adlay (Coix lacryma-jobi L.) and Emerging Applications
by Xiaoyu Yang, Jian Zhang, Maohong Ao, Jing Lei and Chenglong Yang
Plants 2026, 15(2), 320; https://doi.org/10.3390/plants15020320 - 21 Jan 2026
Viewed by 171
Abstract
Adlay (Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains [...] Read more.
Adlay (Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains targeted genetic improvement. While transformative technologies, specifically micro-computed tomography (micro-CT) imaging combined with AI-assisted analysis (e.g., Segment Anything Model (SAM)) and multi-omics approaches, have been successfully applied to unravel the structural and physiological complexities of model cereals, their systematic adoption in adlay research remains fragmented. Going beyond a traditional synthesis of these methodologies, this article proposes a novel, multidimensional framework specifically designed for adlay. This forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data to bridge the gap between macroscopic caryopsis architecture and microscopic metabolic accumulation. By offering a precise digital solution to elucidate adlay’s unique developmental mechanisms, the proposed framework aims to accelerate precision breeding and advance the scientific modernization of this promising underutilized crop. Full article
(This article belongs to the Special Issue AI-Driven Machine Vision Technologies in Plant Science)
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