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Utilizing Artificial Intelligence (AI) Technology for Plant Phenotyping Research
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Utilizing Artificial Intelligence (AI) Technology for Plant Phenotyping Research

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1665

Special Issue Editor

Dr. Mingquan Ding

E-Mail Website
Guest Editor
The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Linan District, Hangzhou 311300, China
Interests: bioinformatics; genomics; development; abiotic stress
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Using Artificial Intelligence (AI) in plant phenotyping is changing the way we study plant biology, allowing us to explore and understand it on a much larger and more detailed scale than ever before. Phenotyping, the process of measuring observable traits in plants, has traditionally been labor-intensive and limited in scope. However, AI technologies, particularly machine learning (ML) and computer vision, are revolutionizing this field by enabling high-throughput, non-invasive, and precise analysis of plant phenomes. AI can process large datasets from various sources such as images, sensors, and multi-omics data to identify complex patterns and correlations that would be difficult to detect through conventional methods. 

This Special Issue highlights cutting-edge applications of AI in plant phenotyping, showcasing how these technologies are advancing our understanding of plant growth, development, and adaptation. From improving crop breeding through more accurate trait identification to predicting plant responses to environmental stress, AI-driven phenotyping offers new insights that enhance both fundamental research and applied agricultural practices. Furthermore, the ability of AI systems to integrate multi-dimensional data accelerates the discovery of genotype–phenotype relationships, aiding in the development of crops with improved yields, disease resistance, and environmental resilience. This Special Issue provides a comprehensive view of the state-of-the-art techniques and future directions in AI-assisted plant phenotyping, emphasizing its critical role in advancing the field of plant research. 

Dr. Mingquan Ding
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. 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

  • artificial intelligence
  • plant phenotype
  • plant development
  • plant stress response

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Published Papers (2 papers)

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22 pages, 5179 KiB  
Article
Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features
by Yuchen Wang, Jianliang Wang, Jiayue Li, Jiacheng Wang, Hanzeyu Xu, Tao Liu and Juan Wang
Plants 2025, 14(6), 973; https://doi.org/10.3390/plants14060973 - 20 Mar 2025
Viewed by 366
Abstract
Leaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimating maize [...] Read more.
Leaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimating maize LWC utilizing UAV-based multispectral imagery combined with a Random Forest Regression (RFR) model. By extracting vegetation indices, image coverage, and texture features and integrating them with ground-truth data, the study examines the variation in LWC estimation accuracy across different growth stages. The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. Moreover, the RFR model consistently outperforms multiple linear regression (MLR) and ridge regression (RR) models throughout the growing season, demonstrating lower errors on both training and testing datasets. Notably, the RFR model exhibits significantly reduced errors in the training dataset compared to both MLR and RR models. Following particle swarm optimization (PSO), the prediction accuracy of the RFR model is notably enhanced, with the RRMSE on the training dataset decreasing from 1.46% to 1.19%. This study provides an effective approach for estimating maize LWC across different growth stages, supporting crop water management and precision agriculture, and offering valuable insights for the estimation of water content in other crops. Full article
(This article belongs to the Special Issue Utilizing Artificial Intelligence (AI) Technology for Plant Phenotyping Research)
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16 pages, 8192 KiB  
Article
Improved CSW-YOLO Model for Bitter Melon Phenotype Detection
by Haobin Xu, Xianhua Zhang, Weilin Shen, Zhiqiang Lin, Shuang Liu, Qi Jia, Honglong Li, Jingyuan Zheng and Fenglin Zhong
Plants 2024, 13(23), 3329; https://doi.org/10.3390/plants13233329 - 27 Nov 2024
Cited by 1 | Viewed by 873
Abstract
As a crop with significant medicinal value and nutritional components, the market demand for bitter melon continues to grow. The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm [...] Read more.
As a crop with significant medicinal value and nutritional components, the market demand for bitter melon continues to grow. The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm resources crucial for breeding work. To address the limitations of time-consuming and less accurate traditional manual identification methods, there is a need to enhance the automation and intelligence of bitter melon phenotype detection. This study developed a bitter melon phenotype detection model named CSW-YOLO. By incorporating the ConvNeXt V2 module to replace the backbone network of YOLOv8, the model’s focus on critical target features is enhanced. Additionally, the SimAM attention mechanism was introduced to compute attention weights for neurons without increasing the parameter count, further enhancing the model’s recognition accuracy. Finally, WIoUv3 was introduced as the bounding box loss function to improve the model’s convergence speed and positioning capabilities. The model was trained and tested on a bitter melon image dataset, achieving a precision of 94.6%, a recall of 80.6%, a mAP50 of 96.7%, and an F1 score of 87.04%. These results represent improvements of 8.5%, 0.4%, 11.1%, and 4% in precision, recall, mAP50, and F1 score, respectively, over the original YOLOv8 model. Furthermore, the effectiveness of the improvements was validated through heatmap analysis and ablation experiments, demonstrating that the CSW-YOLO model can more accurately focus on target features, reduce false detection rates, and enhance generalization capabilities. Comparative tests with various mainstream deep learning models also proved the superior performance of CSW-YOLO in bitter melon phenotype detection tasks. This research provides an accurate and reliable method for bitter melon phenotype identification and also offers technical support for the visual detection technologies of other agricultural products. Full article
(This article belongs to the Special Issue Utilizing Artificial Intelligence (AI) Technology for Plant Phenotyping Research)
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