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Horticulturae
Special Issues
Application of Artificial Intelligence in the Processing of Horticultural Crops
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Application of Artificial Intelligence in the Processing of Horticultural Crops

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Processed Horticultural Products".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 79

Special Issue Editor

Dr. Xiaoyu Tian

E-Mail Website
Guest Editor
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China
Interests: hyperspectral imaging; machine vision; colorimetric sensing；intelligent sensory evaluation of agricultural products and food quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of artificial intelligence (AI) in the processing of horticultural crops integrates advanced multi-sensor technologies and intelligent control systems to enable precise monitoring, optimized handling, and quality enhancement throughout the stages of harvesting, preservation, storage, processing, and quality detection. This interdisciplinary approach promotes automation, enhances product consistency, and supports the sustainable development of horticultural production.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Intelligent harvesting: The application of AI-driven recognition and localization technologies (e.g., computer vision, deep learning, robotic systems) in the automated harvesting of horticultural crops.
  • Intelligent preservation: The integration of IoT-based environmental sensing and predictive modeling for the dynamic control of preservation conditions.
  • Intelligent storage: The development of multi-sensor fusion systems and AI algorithms for the real-time monitoring and modeling of crop status during storage.
  • Intelligent quality detection: The application of multi-modal sensing technologies (e.g., imaging, spectroscopy, olfaction, photoelectric sensors) combined with AI models for the comprehensive, non-destructive quality assessment and grading of horticultural products.

Dr. Xiaoyu Tian
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. 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

  • artificial intelligence
  • horticultural crop processing
  • automated harvesting
  • postharvest preservation
  • storage environment monitoring
  • sensor fusion technology
  • process control and optimization
  • non-destructive quality assessment
  • computer vision
  • multimodal sensing
  • machine learning algorithms

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Published Papers (1 paper)

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Research

21 pages, 41202 KiB  
Article
Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images
by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai and Yubin Xie
Horticulturae 2025, 11(7), 840; https://doi.org/10.3390/horticulturae11070840 - 16 Jul 2025
Abstract
In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to [...] Read more.
In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F1, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the Processing of Horticultural Crops)
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