Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.6
4.1
Journals
Plants
Special Issues
Application of Non-Contact Detection and Artificial Intelligence Techniques to Estimate...
share announcement

Application of Non-Contact Detection and Artificial Intelligence Techniques to Estimate Quality and Longevity of Horticultural Plants

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Horticultural Science and Ornamental Plants".

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 5908

Special Issue Editor

Dr. Byung-Chun In

E-Mail Website
Guest Editor
Department of Smart Horticultural Science, Andong National University, Andong, Republic of Korea
Interests: plant physiology; horticulture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid and accurate horticultural product quality assessment is becoming increasingly important as e-commerce continues to expand globally. Recently, non-contact detection (NCD) techniques, including hyperspectral photography, thermography, and NIR spectrometry, have become widely applied for assessing crop quality attributes. Artificial intelligence (AI) techniques, including deep learning based on algorithms, have also been employed to develop assessment methods because they can predict specific factors based on images obtained using NCD methods. Converging interdisciplinary research involving plant science and AI techniques is becoming increasingly important as it provides creative solutions to develop rapid and accurate tools that can classify horticultural products for quality assurance systems. This Special Issue of Plants deals with the application of NCD methods and AI models to assess as well as predict external and internal quality factors in horticultural plants.

Dr. Byung-Chun In
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 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

  • artificial intelligence
  • AI
  • deep learning
  • hyperspectral imaging
  • non-contact
  • non-destructive
  • prediction
  • postharvest
  • quality
  • longevity

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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 (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 6244 KB  
Article
Detection and Maturity Classification of Dense Small Lychees Using an Improved Kolmogorov–Arnold Network–Transformer
by Zhenpeng Zhang, Yi Wang, Shanglei Chai and Yibin Tian
Plants 2025, 14(21), 3378; https://doi.org/10.3390/plants14213378 - 4 Nov 2025
Viewed by 481
Abstract
Lychee detection and maturity classification are crucial for yield estimation and harvesting. In densely packed lychee clusters with limited training samples, accurately determining ripeness is challenging. This paper proposes a new transformer model incorporating a Kolmogorov–Arnold Network (KAN), termed GhostResNet (GRN)–KAN–Transformer, for lychee [...] Read more.
Lychee detection and maturity classification are crucial for yield estimation and harvesting. In densely packed lychee clusters with limited training samples, accurately determining ripeness is challenging. This paper proposes a new transformer model incorporating a Kolmogorov–Arnold Network (KAN), termed GhostResNet (GRN)–KAN–Transformer, for lychee detection and ripeness classification in dense on-tree fruit clusters. First, within the backbone, we introduce a stackable multi-layer GhostResNet module to reduce redundancy in feature extraction and improve efficiency. Next, during feature fusion, we add a large-scale layer to enhance sensitivity to small objects and to increase polling of the small-scale feature map during querying. We further propose a multi-layer cross-fusion attention (MCFA) module to achieve deeper hierarchical feature integration. Finally, in the decoding stage, we employ an improved KAN for the classification and localization heads to strengthen nonlinear mapping, enabling a better fitting to the complex distributions of categories and positions. Experiments on a public dataset demonstrate the effectiveness of GRN-KANformer. Compared with the baseline, GFLOPs and parameters of the model are reduced by 8.84% and 11.24%, respectively, while mean Average Precision (mAP) metrics mAP50 and mAP50–95 reach 94.7% and 58.4%, respectively. Thus, it lowers computational complexity while maintaining high accuracy. Comparative results against popular deep learning models, including YOLOv8n, YOLOv12n, CenterNet, and EfficientNet, further validate the superior performance of GRN-KANformer. Full article
(This article belongs to the Special Issue Application of Non-Contact Detection and Artificial Intelligence Techniques to Estimate Quality and Longevity of Horticultural Plants)
►▼ Show Figures

Figure 1

17 pages, 13361 KB  
Article
Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras
by Ji Yeong Ham, Yong-Tae Kim, Suong Tuyet Thi Ha and Byung-Chun In
Plants 2025, 14(7), 1076; https://doi.org/10.3390/plants14071076 - 1 Apr 2025
Cited by 1 | Viewed by 2129
Abstract
Here, we developed a vase-life monitoring system (VMS) to automatically and accurately assess the post-harvest quality and vase life (VL) of cut roses. The VMS integrates camera imaging with the YOLOv8 (You Only Look Once version 8) deep learning algorithm to continuously monitor [...] Read more.
Here, we developed a vase-life monitoring system (VMS) to automatically and accurately assess the post-harvest quality and vase life (VL) of cut roses. The VMS integrates camera imaging with the YOLOv8 (You Only Look Once version 8) deep learning algorithm to continuously monitor major physiological parameters including flower opening, fresh weight, water uptake, and gray mold disease incidence. Our results showed that the VMS can automatically measure the main physiological factors of cut roses by obtaining precise and consistent data. The values measured for physiology and disease by the VMS closely correlated with those measured by observation (OBS). Additionally, YOLOv8 achieved a high performance in the model by obtaining an object detection accuracy of 90%. Additionally, the mAP0.5 supported the high accuracy of the model in evaluating the VL of cut roses. Regression analysis revealed a strong correlation between the VL, VMS, and OBS. The VMS incorporating the microscope detected physiological and disease factors in the early stages of development. These results show that the plant monitoring system incorporating a microscope is highly effective for evaluating the post-harvest quality of cut roses. The early detection method using the VMS could also be applied to the flower breeding process, which requires rapid measurements of important characteristics of flower species, such as VL and disease resistance, to develop superior cultivars. Full article
(This article belongs to the Special Issue Application of Non-Contact Detection and Artificial Intelligence Techniques to Estimate Quality and Longevity of Horticultural Plants)
►▼ Show Figures

Figure 1

19 pages, 29968 KB  
Article
Ripe Tomato Detection Algorithm Based on Improved YOLOv9
by Yan Wang, Qianjie Rong and Chunhua Hu
Plants 2024, 13(22), 3253; https://doi.org/10.3390/plants13223253 - 20 Nov 2024
Cited by 16 | Viewed by 2802
Abstract
Recognizing ripe tomatoes is a crucial aspect of tomato picking. To ensure the accuracy of inspection results, You Only Look Once version 9 (YOLOv9) has been explored as a fruit detection algorithm. To tackle the challenge of identifying tomatoes and the low accuracy [...] Read more.
Recognizing ripe tomatoes is a crucial aspect of tomato picking. To ensure the accuracy of inspection results, You Only Look Once version 9 (YOLOv9) has been explored as a fruit detection algorithm. To tackle the challenge of identifying tomatoes and the low accuracy of small object detection in complex environments, we propose a ripe tomato recognition algorithm based on an enhanced YOLOv9-C model. After collecting tomato data, we used Mosaic for data augmentation, which improved model robustness and enriched experimental data. Improvements were made to the feature extraction and down-sampling modules, integrating HGBlock and SPD-ADown modules into the YOLOv9 model. These measures resulted in high detection performance with precision and recall rates of 97.2% and 92.3% in horizontal and vertical experimental comparisons, respectively. The module-integrated model improved accuracy and recall by 1.3% and 1.1%, respectively, and also reduced inference time by 1 ms compared to the original model. The inference time of this model was 14.7 ms, which is 16 ms better than the RetinaNet model. This model was tested accurately with mAP@0.5 (%) up to 98%, which is 9.6% higher than RetinaNet. Its increased speed and accuracy make it more suitable for practical applications. Overall, this model provides a reliable technique for recognizing ripe tomatoes during the picking process. Full article
(This article belongs to the Special Issue Application of Non-Contact Detection and Artificial Intelligence Techniques to Estimate Quality and Longevity of Horticultural Plants)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop