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Plants
Special Issues
Plant Phenotyping and Machine Learning
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Plant Phenotyping and Machine Learning

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 877

Special Issue Editors

Dr. Lingfeng Duan

E-Mail Website
Guest Editor
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
Interests: plant phenotyping; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ni Jiang

E-Mail Website
Co-Guest Editor
Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
Interests: plant phenomics; plant phenotyping; deep learning and computer vision in agriculture; high-throughput imaging technologies

Special Issue Information

Dear Colleagues,

Plant phenotyping quantifies the structural and functional attributes of plants, essential for non-destructive and continuous crop morphology and physiology analysis. The complexity of plant growth, including variations in size, morphology, color, texture, and organ development, as well as the impact of environmental factors, poses challenges in plant phenotyping. Machine learning, particularly deep learning, offers robust solutions for complex issues, providing new tools for analyzing, predicting, and understanding plant traits. This Special Issue invites submissions addressing plant phenotyping and machine learning. Specific topics of interest include, but not limited to, the following:

  1. Image-based phenotyping, including plant classification, segmentation, and detection.
  2. Extraction and selection of plant phenotypic traits.
  3. Plant growth monitoring, analysis, modeling, and prediction.
  4. Disease detection and monitoring.
  5. Assessment and prediction of plant resistance (including biotic and abiotic stresses).
  6. Temporal image processing and analysis of plants.
  7. Data analysis algorithm and software for plant phenotyping.
  8. Phenotype prediction and classification.

Dr. Lingfeng Duan
Dr. Ni Jiang
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 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

  • plant phenotyping
  • machine learning
  • image processing
  • growth analysis
  • data analysis

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Published Papers (1 paper)

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Research

21 pages, 9664 KB  
Article
A Detection Approach for Wheat Spike Recognition and Counting Based on UAV Images and Improved Faster R-CNN
by Donglin Wang, Longfei Shi, Huiqing Yin, Yuhan Cheng, Shaobo Liu, Siyu Wu, Guangguang Yang, Qinge Dong, Jiankun Ge and Yanbin Li
Plants 2025, 14(16), 2475; https://doi.org/10.3390/plants14162475 - 9 Aug 2025
Viewed by 414
Abstract
This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 [...] Read more.
This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R2 = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m2) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article
(This article belongs to the Special Issue Plant Phenotyping and Machine Learning)
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