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Horticulturae
Special Issues
AI-Powered Phenotyping of Horticultural Plants
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AI-Powered Phenotyping of Horticultural Plants

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Genetics, Genomics, Breeding, and Biotechnology (G2B2)".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3003

Special Issue Editors

Dr. Xu 'Kevin' Wang

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, IFAS Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, USA
Interests: phenomics for plant breeding; machine learning; computer vision; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Shinsuke Agehara

E-Mail Website
Guest Editor
Horticultural Sciences Department, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, FL, USA
Interests: plant physiology; plant morphology; plant phenology; soil fertility; acclimation
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Zhang

E-Mail Website
Guest Editor
Horticultural Science Department, North Carolina State University, Raleigh, NC, USA
Interests: plant physiology; high-throughput phenotyping; precision crop management

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) in phenotyping has revolutionized horticultural science, offering innovative solutions to enhance crop productivity, sustainability, and resilience. As the global demand for horticultural products continues to rise, driven by population growth and the need for nutritional security, it is imperative to develop advanced phenotyping techniques that leverage AI to address these challenges effectively.

This Special Issue focuses on the applications of AI-assisted phenotyping in horticulture, aiming to showcase cutting-edge research and developments that push the boundaries of traditional horticultural practices. We invite original research articles, comprehensive reviews, and insightful perspectives that explore the following key areas:

  • AI-Driven Image Analysis and Pattern Recognition: Using machine learning and computer vision to analyze plant traits, detect diseases, and monitor growth patterns with high accuracy.
  • Precision Agriculture: Implementing AI for site-specific management, optimizing resource use, and minimizing environmental impacts through precision phenotyping.
  • Genomic Selection and Breeding: Integrating AI with genomic data to predict desirable traits and accelerate the development of superior horticultural varieties.
  • Stress Tolerance and Adaptation: Using AI to understand and improve plant responses to biotic and abiotic stresses, enhancing crop resilience.
  • Robotics and Automation in Phenotyping: Exploring autonomous systems and robots for high-throughput phenotyping, reducing labor costs, and increasing data collection efficiency.
  • Data Integration and Management: Developing AI-driven platforms to manage and analyze diverse phenotypic and environmental data sets.

Dr. Xu 'Kevin' Wang
Dr. Shinsuke Agehara
Dr. Jing Zhang
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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

  • phenotyping
  • artificial intelligence
  • precision agriculture
  • horticultural science

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

Published Papers (2 papers)

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Research

12 pages, 1023 KiB  
Article
Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study
by Serkan Ozdemir
Horticulturae 2025, 11(3), 308; https://doi.org/10.3390/horticulturae11030308 - 12 Mar 2025
Viewed by 1059
Abstract
To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of [...] Read more.
To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As a result, there is a gradual adoption of new generative artificial intelligence (AI) tools in the field of horticulture. These tools are expected to minimize bias in human perception when determining the quality of a watermelon based on its outer characteristics. This study aimed to compare the quality of watermelons selected by generative AI with a panel sensory evaluation test. The results of the two case studies indicate a significant difference in the quality of the generative AI-selected watermelons. As an average, watermelon evaluators favored the watermelons selected by ChatGPT as the best based on the Wilcoxon rank sum test and paired t-test (p < 0.05). In conclusion, watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations. Full article
(This article belongs to the Special Issue AI-Powered Phenotyping of Horticultural Plants)
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14 pages, 11542 KiB  
Article
Open-Source High-Throughput Phenotyping for Blueberry Yield and Maturity Prediction Across Environments: Neural Network Model and Labeled Dataset for Breeders
by Jing Zhang, Jerome Maleski, Hudson Ashrafi, Jessica A. Spencer and Ye Chu
Horticulturae 2024, 10(12), 1332; https://doi.org/10.3390/horticulturae10121332 - 13 Dec 2024
Viewed by 1311
Abstract
Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and [...] Read more.
Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is categorical and subjective. In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. We aim to facilitate further research in computer vision and precision agriculture by publishing the labeled image dataset and the trained model. In this research, true-color images of blueberry bushes were collected, annotated, and used to train a deep neural network object detection model [You Only Look Once (YOLOv11)] to detect mature and immature berries. Different versions of YOLOv11 were used, including nano, small, and medium, which had similar performance, while the medium version had slightly higher metrics. The YOLOv11m model shows strong performance for the mature berry class, with a precision of 0.90 and an F1 score of 0.90. The precision and recall for detecting immature berries were 0.81 and 0.79. The model was tested on 10 blueberry bushes by hand harvesting and weighing blueberries. The results showed that the model detects approximately 25% of the berries on the bushes, and the correlation coefficients between model-detected and hand-harvested traits were 0.66, 0.86, and 0.72 for mature fruit count, immature fruit count, and mature ratio, respectively. The model applied to 91 blueberry advance selections and categorized them into groups with diverse levels of maturity and productivity using principal component analysis (PCA). These results inform the harvest window and yield of these breeding lines with precision and objectivity through berry classification and quantification. This model will be helpful for blueberry breeders, enabling more efficient selection, and for growers, helping them accurately estimate optimal harvest windows. This open-source tool can potentially enhance research capabilities and agricultural productivity. Full article
(This article belongs to the Special Issue AI-Powered Phenotyping of Horticultural Plants)
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