Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Application of Spectral Sensors in Agricultural Product Quality Detection
share announcement

Application of Spectral Sensors in Agricultural Product Quality Detection

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Product Quality and Safety".

Deadline for manuscript submissions: 5 June 2025 | Viewed by 2116

Special Issue Editor

Dr. Tong Lei

E-Mail Website
Guest Editor
Plant Simulation Lab, Department of Plant Sciences, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
Interests: plant 3D modeling; optical sensing; machine learning; biosystems engineering; radiation transfer

Special Issue Information

Dear Colleagues,

The utilization of spectral sensors in agriculture has revolutionized the way producers, scientists, and engineers approach crop management and food safety. This Special Issue aims to highlight innovative research and developments in the application of spectral sensors for detecting and predicting the quality of agricultural products. It seeks to cover a broad spectrum of topics, including advances in sensor technology, data analysis techniques, and their integration into existing agricultural practices. Papers in this issue will showcase the latest breakthroughs in spectral sensor technology and its applications in agriculture. This includes novel sensor designs, improvements in data processing algorithms, and the use of artificial intelligence to interpret complex datasets. Suitable topics include, but are not limited to, the following:

  1. Development and validation of new spectral sensors for quality detection in various crops;
  2. Case studies on the implementation of spectral sensors in field conditions;
  3. Methods for interpreting complex data collected by spectral sensors, including the use of machine learning and artificial intelligence to predict crop quality.

Dr. Tong Lei
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. Agriculture 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 2600 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

  • VIS-NIR
  • spectroscopy
  • machine learning
  • agricultural product

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices 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

19 pages, 3255 KiB  
Article
Advancing Loquat Total Soluble Solids Content Determination by Near-Infrared Spectroscopy and Explainable AI
by Yizhi Luo, Qingting Jin, Huazhong Lu, Peng Li, Guangjun Qiu, Haijun Qi, Bin Li and Xingxing Zhou
Agriculture 2025, 15(3), 281; https://doi.org/10.3390/agriculture15030281 - 28 Jan 2025
Viewed by 321
Abstract
TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900–1700 nm near-infrared spectroscopy of [...] Read more.
TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900–1700 nm near-infrared spectroscopy of 156 fresh loquat samples was collected and preprocessed using seven preprocessing techniques, significant wavelength extraction utilizing six feature methods to eliminate data redundancy. Linear and nonlinear models were employed to establish the relationship between the feature spectrum and TSSC, with a focus on comparing and analyzing prediction performance. The findings reveal that the combination of 26 spectral bands selected by SPA and the PLSR model yielded the best prediction outcomes (R = 0.9031, RMSEP = 0.6171, RPD = 2.2803). The contribution of key wavelengths can be obtained by SHAP, which explains differences in model prediction accuracy and provides a reference for the application of loquat TSSC determination. Full article
(This article belongs to the Special Issue Application of Spectral Sensors in Agricultural Product Quality Detection)
►▼ Show Figures

Graphical abstract

22 pages, 6345 KiB  
Article
Fast Dynamic Time Warping and Hierarchical Clustering with Multispectral and Synthetic Aperture Radar Temporal Analysis for Unsupervised Winter Food Crop Mapping
by Hsuan-Yi Li, James A. Lawarence, Philippa J. Mason and Richard C. Ghail
Agriculture 2025, 15(1), 82; https://doi.org/10.3390/agriculture15010082 - 2 Jan 2025
Viewed by 648
Abstract
Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative [...] Read more.
Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article
(This article belongs to the Special Issue Application of Spectral Sensors in Agricultural Product Quality Detection)
►▼ Show Figures

Figure 1

21 pages, 30819 KiB  
Article
Multisensor Analysis for Biostimulants Effect Detection in Sustainable Viticulture
by Alberto Sassu, Alessandro Deidda, Luca Mercenaro, Beatrice Virgillito and Filippo Gambella
Agriculture 2024, 14(12), 2221; https://doi.org/10.3390/agriculture14122221 - 5 Dec 2024
Viewed by 715
Abstract
Biostimulants are organic agents employed for crop yield enhancement, quality improvement, and environmental stress mitigation, reducing, at the same time, reliance on inorganic inputs. With advancements in sustainable agriculture, data acquisition technologies have become crucial for monitoring the effects of such inputs. This [...] Read more.
Biostimulants are organic agents employed for crop yield enhancement, quality improvement, and environmental stress mitigation, reducing, at the same time, reliance on inorganic inputs. With advancements in sustainable agriculture, data acquisition technologies have become crucial for monitoring the effects of such inputs. This study evaluates the impact of four increasing rates of Biopromoter biostimulant application on grapevines: 0, 100 g plant−1, 100 g plant−1 with additional foliar fertilizers, and 150 g plant−1 with additional foliar fertilizers. The biostimulant was applied via foliar or ground methods, and its effects were assessed using vegetation indices derived from unmanned aerial systems (UAS), as well as proximal and manual sensing tools, alongside qualitative and quantitative production metrics. The research was conducted over two seasons in a Malvasia Bianca vineyard in Sardinia, Italy. Results indicated that UAS-derived vegetation indices, consistent with traditional ground-based measurements, effectively monitored vegetative growth over time but revealed no significant differences between treatments, suggesting either an insufficient vegetative indices sensitivity or that the applied biostimulant rates were insufficient to elicit a measurable response in the cultivar. Among the tools employed, only the SPAD 502 m demonstrated the sensitivity required to detect treatment differences, primarily reflected in grape production outcomes, especially in the second year and in the two groups managed with the highest amounts of biostimulants distributed by foliar and soil application. The use of biostimulants promoted, although only in the second year, a greener canopy and higher productivity in treatments where it was delivered to the soil. Further agronomic experiments are required to improve knowledge about biostimulants’ composition and mode of action, which are essential to increasing their effectiveness against specific abiotic stresses. Future research will focus on validating these technologies for precision viticulture, particularly concerning the long-term benefits. Full article
(This article belongs to the Special Issue Application of Spectral Sensors in Agricultural Product Quality Detection)
►▼ 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