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Plants
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Machine Learning for Plant Phenotyping in Crops
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Machine Learning for Plant Phenotyping in Crops

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2343

Special Issue Editors

Dr. Qing Li

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Guest Editor
Regional Technique Innovation Center for Wheat Production, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: wheat; abiotic stress; crop production; plant phenotyping; deep learning
Prof. Dr. Ping Liu

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Guest Editor
Shandong Engineering Research Center of Agricultural Equipment Intelligentization, Shandong Key Laboratory of Intelligent Production Technology and Equipment for Facility Horticulture, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Interests: intelligent control technology for agricultural equipment; research on key technologies of robots; intelligent detection technology

Special Issue Information

Dear Colleagues,

In recent years, the integration of automation control and optical sensing technologies has led to the development of various high-throughput, non-destructive phenotyping platforms, including handheld, gantry, and drone-based systems for both indoor and outdoor applications. These advancements have established a multi-scale, comprehensive plant temporal phenotyping framework, spanning from above- to below-ground, from morphological to physiological, and from macroscopic to microscopic levels. These significantly improve data collection efficiency and precision, while also causing an explosion in phenotypic data.

Precise phenotypic analysis of crops is crucial for genetic improvement and high-yield cultivation. Researchers have amassed vast crop 2D images (visible light, multispectral/hyperspectral, thermal infrared, and chlorophyll fluorescence) and 3D point cloud data, which urgently require in-depth exploration. Machine learning (ML) and its subfield, deep learning (DL), have played pivotal roles in processing complex data, thereby providing powerful tools for efficient, accurate, and automated analysis of large-scale phenotypic datasets. Therefore, this Special Issue aims to explore and showcase the latest research progress, methodologies, and applications of ML in crop phenotyping studies, focusing on advancements that improving crop genetic breeding and high-efficiency production management. Topics include, but are not limited to, the application of ML and DL techniques in the following:

  • Micro-scale phenotypic trait analysis: e.g., morphology of stomata, starch granules, and other physiological micro-indicators.
  • Organ-scale phenotypic trait analysis: e.g., seed, root, stem, leaf, and spike.
  • Individual-scale phenotypic trait analysis: e.g., plant architecture and phenological stages.
  • Population-scale phenotypic trait analysis: e.g., uniformity and yield.
  • Applications in analyzing other critical 2D and 3D crop phenotypic traits.
  • Development of predictive/estimation models for key crop phenotypic traits.
  • Genotype–phenotype associations: Integration of ML with genomic data to predict phenotypic outcomes, thereby supporting marker-assisted breeding and genomic selection.

We look forward to receiving your contributions.

Dr. Qing Li
Prof. Dr. Ping Liu
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

  • machine learning
  • deep learning
  • multi-scale phenotype
  • multi-data analysis
  • modeling

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Published Papers (4 papers)

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Research

25 pages, 2058 KB  
Article
Integrating Multi-Source and Multi-Temporal UAV Observations to Improve Wheat Yield Prediction Using Machine Learning
by Chen Chen, Jiajun Liu, Yao Deng, Rui Guo, Weicheng Yao, Tianle Yang, Weijun Zhang, Tao Liu, Xiuliang Jin, Wei Xiong and Dongsheng Li
Plants 2026, 15(9), 1345; https://doi.org/10.3390/plants15091345 - 28 Apr 2026
Abstract
Accurate yield estimation is vital for precision wheat management and breeding. Traditional methods based on single growth stages or single-source data cannot capture cumulative growth effects, limiting prediction accuracy. UAV remote sensing provides high-resolution, multi-source, and multi-temporal data, enabling improved non-destructive yield estimation. [...] Read more.
Accurate yield estimation is vital for precision wheat management and breeding. Traditional methods based on single growth stages or single-source data cannot capture cumulative growth effects, limiting prediction accuracy. UAV remote sensing provides high-resolution, multi-source, and multi-temporal data, enabling improved non-destructive yield estimation. In this study, UAV-based multispectral and RGB imagery were collected at six key growth stages, and vegetation indices, texture, and color features were extracted to develop yield prediction models using RF, XGBoost, and KNN under single- and multi-temporal scenarios. The results showed that red-edge-based vegetation indices were highly sensitive to wheat yield and outperformed texture- and color-based features. Multi-feature fusion further improved prediction accuracy at key growth stages, particularly during booting and flowering (R2 = 0.53–0.67). Compared with single-temporal models, multi-temporal data fusion significantly enhanced yield estimation accuracy, achieving a maximum R2 of 0.72 by integrating data from the late-jointing, booting and flowering stages. Among the algorithms, XGBoost and KNN exhibited superior accuracy and stability across most growth stages. Overall, these results demonstrate that integrating UAV-based multi-source and multi-temporal remote sensing data effectively improves the accuracy and robustness of wheat yield estimation, providing valuable technical support for precision agriculture and phenotyping-assisted breeding. Full article
(This article belongs to the Special Issue Machine Learning for Plant Phenotyping in Crops)
23 pages, 6865 KB  
Article
Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods
by Guanzi Zhou, Haotian Ji, Rongyu Pan, Xiaowei Yang, Suhui Zhao, Lei Yang, Xiaohan Shang, Huijie Zhang, Hanchi Zhang, Xiaojun Liu, Yuanchun Ma, Xujun Zhu, Jie Jiang and Wanping Fang
Plants 2026, 15(7), 1071; https://doi.org/10.3390/plants15071071 - 31 Mar 2026
Viewed by 456
Abstract
Achieving rapid and non-destructive assessment of tea quality is essential for intelligent tea production and quality control. In this study, an integrated hyperspectral and deep learning framework was developed to estimate tea quality constituents across seasons and physical states. Samples included field fresh [...] Read more.
Achieving rapid and non-destructive assessment of tea quality is essential for intelligent tea production and quality control. In this study, an integrated hyperspectral and deep learning framework was developed to estimate tea quality constituents across seasons and physical states. Samples included field fresh leaves, dried tea leaves, and tea powder, were collected in spring, summer, and autumn. Tea polyphenols and catechins were predicted using original reflectance, harmonic features, and wavelet features fused into multi-domain indices. Extreme gradient boosting, Gaussian process regression, and convolutional neural networks (CNN) were systematically compared to construct the quality estimation models. The result showed that three-feature indices consistently outperformed two-feature indices, yielding R2 from 0.48 to 0.71. CNN achieved the best overall performance among the three modeling approaches, with its optimal accuracy obtained for tea powder samples in autumn, yielding R2 values of 0.81 and 0.76 for tea polyphenols and catechins, respectively. This framework provides an accurate, non-destructive tool for tea quality evaluation and traceability, offering technical support for intelligent agriculture and quality control across the tea industry chain. Full article
(This article belongs to the Special Issue Machine Learning for Plant Phenotyping in Crops)
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18 pages, 11417 KB  
Article
Comparative Evaluation of Allometric, Machine Learning, and Ensemble Approaches for Modeling Dynamic Structure–Fresh Weight Relationships in Sweet Pepper
by Jun Hyeun Kang and Taewon Moon
Plants 2026, 15(7), 1063; https://doi.org/10.3390/plants15071063 - 31 Mar 2026
Viewed by 426
Abstract
Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet [...] Read more.
Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper and identify which approach is most suitable under conditions of dynamic biomass partitioning. Non-destructive morphological measurements were collected under greenhouse cultivation, and allometric models based on geometric equations were established as baselines. Their performance was compared with machine learning (ML) models and ensemble learning frameworks. To address limited data availability, numerical data augmentation with Gaussian noise and a variational autoencoder was applied. Among the allometric models, the stick model combined with a sigmoid function showed the highest performance, with an R2 of 0.80 for shoot FW and 0.54 for fruit FW. All ML models outperformed the allometric models, and the ensemble model achieved the highest predictive accuracy, with an R2 of 0.96 for shoot FW and 0.89 for fruit FW. Data augmentation further improved predictive performance across all ML models, particularly for fruit FW prediction. Feature contribution analysis revealed that temporal progression was the dominant predictor of fruit FW, while structural traits played the primary role in shoot FW estimation. Ensemble-based ML, combined with data augmentation, provides a methodological framework for non-destructive FW estimation of sweet pepper in controlled environments such as greenhouses and smart farming systems. Full article
(This article belongs to the Special Issue Machine Learning for Plant Phenotyping in Crops)
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22 pages, 8042 KB  
Article
WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits
by Honghao Zhou, Haijiang Min, Shaowei Liang, Bingxi Qin, Qi Sun, Zijun Pei, Qiuxiao Pan, Xiao Wang, Jian Cai, Qin Zhou, Yingxin Zhong, Mei Huang, Dong Jiang, Jiawei Chen and Qing Li
Plants 2025, 14(19), 3016; https://doi.org/10.3390/plants14193016 - 29 Sep 2025
Viewed by 993
Abstract
Stomata on the leaves of wheat serve as important gateways for gas exchange with the external environment. Their morphological characteristics, such as size and density, are closely related to physiological processes like photosynthesis and transpiration. However, due to the limitations of existing analysis [...] Read more.
Stomata on the leaves of wheat serve as important gateways for gas exchange with the external environment. Their morphological characteristics, such as size and density, are closely related to physiological processes like photosynthesis and transpiration. However, due to the limitations of existing analysis methods, the efficiency of analyzing and mining stomatal phenotypes and their associated genes still requires improvement. To enhance the accuracy and efficiency of stomatal phenotype traits analysis and to uncover the related key genes, this study selected 210 wheat varieties. A novel semantic segmentation model based on transformer for wheat stomata, called Wheat Stoma Former (WSF), was proposed. This model enables fully automated and highly efficient stomatal mask extraction and accurately analyzes phenotypic traits such as the length, width, area, and number of stomata on both the adaxial (Ad) and abaxial (Ab) surfaces of wheat leaves based on the mask images. The model evaluation results indicate that coefficients of determination (R2) between the predicted values and the actual measurements for stomatal length, width, area, and number were 0.88, 0.86, 0.81, and 0.93, respectively, demonstrating the model’s high precision and effectiveness in stomatal phenotypic trait analysis. The phenotypic data were combined with sequencing data from the wheat 660 K SNP chip and subjected to a genome-wide association study (GWAS) to analyze the genetic basis of stomatal traits, including length, width, and number, on both adaxial and abaxial surfaces. A total of 36 SNP peak loci significantly associated with stomatal traits were identified. Through candidate gene identification and functional analysis, two genes—TraesCS2B02G178000 (on chromosome 2B, related to stomatal number on the abaxial surface) and TraesCS6A02G290600 (on chromosome 6A, related to stomatal length on the adaxial surface)—were found to be associated with stomatal traits involved in regulating stomatal movement and closure, respectively. In conclusion, our WSF model demonstrates valuable advances in accurate and efficient stomatal phenotyping for locating genes related to stomatal traits in wheat and provides breeders with accurate phenotypic data for the selection and breeding of water-efficient wheat varieties. Full article
(This article belongs to the Special Issue Machine Learning for Plant Phenotyping in Crops)
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