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Horticulturae
Special Issues
Application of Non-destructive Detection Techniques in Horticultural Plants
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Application of Non-destructive Detection Techniques in Horticultural Plants

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Postharvest Biology, Quality, Safety, and Technology".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 2168

Special Issue Editors

Dr. Danial Fatchurrahman

E-Mail Website
Guest Editor
Department of Agriculture, Food, Natural Science, Engineering, University of Foggia, Via Napoli 25, 71122 Foggia, Italy
Interests: spectroscopy; image acquisition; digital image processing product packaging; postharvest technique; chemometrics
Dr. Laifeng Lu

E-Mail Website
Guest Editor
College of Food Science and Engineering, Tianjin University Science and Technology, State Key Laboratory of Food Nutrition and Safety, Tianjin 300457, China
Interests: postharvest physiology; ripening and senescence; postharvest pathology; immune response of fruits; storage and processing of agriculture products; fruit microbiome
Special Issues, Collections and Topics in MDPI journals
Dr. Anisur Rahman

E-Mail Website
Guest Editor
Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
Interests: hyperspectral imaging; spectral analysis; chemometrics; nondestructive sensing

Special Issue Information

Dear Colleagues,

Non-destructive detection techniques have recently emerged as a powerful analytical technique with the advantages of fast speed, convenient operation, and easy online inspection of various horticultural products. In recent years, non-destructive detection techniques (such as visible, near- and mid-infrared spectroscopy (VIS-NIRS), fluorescence spectroscopy, hyperspectral imaging (HSI), X-ray imaging, CT scan imaging, electronic nose, machine vision, and thermal imaging) have found numerous successful applications in horticultural product quality detection. These techniques are used to determine quality features and analyze horticultural products in a non-destructive way with minimal sample preparation. The resulting datasets are usually high dimensional and complex, requiring methods of pattern recognition or predictive analysis to extract quality information. This Special Issue aims to focus on the latest research progress of the application and jointly discuss the focus of non-destructive detection techniques in horticultural products.

Dr. Danial Fatchurrahman
Dr. Laifeng Lu
Dr. Anisur Rahman
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

  • non-destructive detection technique
  • visible spectroscopy
  • near-infrared spectroscopy
  • short-infrared spectroscopy
  • fluorescence spectroscopy
  • hyperspectral imaging
  • X-ray imaging
  • thermal imaging
  • machine vision
  • electronic nose

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Published Papers (2 papers)

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Research

15 pages, 3375 KiB  
Article
Non-Destructive Prediction of Rosmarinic Acid Content in Basil Plants Using a Portable Hyperspectral Imaging System and Ensemble Learning Algorithms
by Hyo In Yoon, Dahye Ryu, Jai-Eok Park, Ho-Youn Kim, Soo Hyun Park and Jung-Seok Yang
Horticulturae 2024, 10(11), 1156; https://doi.org/10.3390/horticulturae10111156 - 31 Oct 2024
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Abstract
Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control [...] Read more.
Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R2P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage. Full article
(This article belongs to the Special Issue Application of Non-destructive Detection Techniques in Horticultural Plants)
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16 pages, 10750 KiB  
Article
Classification of Citrus Leaf Diseases Using Hyperspectral Reflectance and Fluorescence Imaging and Machine Learning Techniques
by Hyun Jung Min, Jianwei Qin, Pappu Kumar Yadav, Quentin Frederick, Thomas Burks, Megan Dewdney, Insuck Baek and Moon Kim
Horticulturae 2024, 10(11), 1124; https://doi.org/10.3390/horticulturae10111124 - 22 Oct 2024
Viewed by 853
Abstract
Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of [...] Read more.
Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of a portable reflectance and fluorescence hyperspectral imaging (HSI) system for detecting and classifying a control group and citrus leaf diseases, including canker, Huanglongbing (HLB), greasy spot, melanose, scab, and zinc deficiency. The HSI system was used to simultaneously collect reflectance and fluorescence images from the front and back sides of the leaves. Nine machine learning classifiers were trained using full spectra and spectral bands selected through principal component analysis (PCA) from the HSI with pixel-based and leaf-based spectra. A support vector machine (SVM) classifier achieved the highest overall classification accuracy of 90.7% when employing the full spectra of combined reflectance and fluorescence data and pixel-based analysis from the back side of the leaves, whereas a discriminant analysis classifier yielded the best accuracy of 94.5% with the full spectra of combined reflectance and fluorescence data and leaf-based analysis. Among the diseases, control, scab, and melanose were classified most accurately, each with over 90% accuracy. Therefore, the integration of the reflectance and fluorescence HSI with advanced machine learning techniques demonstrated the capability to accurately detect and classify these citrus leaf diseases with high precision. Full article
(This article belongs to the Special Issue Application of Non-destructive Detection Techniques in Horticultural Plants)
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