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Agriculture
Special Issues
Nondestructive Sensing Techniques and Intelligence Systems for Agricultural Product Detection
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Nondestructive Sensing Techniques and Intelligence Systems for Agricultural Product 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: closed (20 December 2023) | Viewed by 9727

Special Issue Editors

Dr. Jiangbo Li

E-Mail Website
Guest Editor
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: quality and safety assessment of agricultural products; development of agricultural product grading systems; computer vision and machine learning; hyperspectral and multispectral imaging; near infrared spectroscopy analysis and modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Baohua Zhang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Interests: quality and safety assessment of agricultural products; harvesting robots; robot vision; robotic grasping; spectral analysis and modeling; robotic systems and their applications in agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhiming Guo

E-Mail Website
Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: nondestructive detection of food quality and safety; optical sensing and automation for food quality evaluation; advanced chemometrics methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The quality and safety of agricultural products affect human nutrition and health. The development of advanced detection techniques and intelligence systems is crucial to ensuring high quality, nutritious and safe agricultural products/food supplies. Non-destructive sensing technologies (e.g., optical, thermal, ultrasonic), in conjunction with advanced data analytics (e.g., machine learning, data mining) and control and automation technology, have evolved as a potent means for augmenting existing quality and safety control efforts of agricultural products. With advancements in computer technologies, artificial intelligence, sensors, internet of things, and robotics, recent years have seen the commercial-scale adoption of non-destructive sensing technology (e.g., machine vision, near-infrared spectroscopy) for postharvest quality evaluation for a diversity of specialty crop products (e.g., fruit, vegetable, seed). At present, many intelligent detection and grading systems have been commercial application. However, there are still numerous challenges with robust, high-performance detection and grading of many quality and safety issues. New research needs to be further explored, especially in massive data mining, highly stable model construction, adaptive learning algorithm development, advanced inspection system design, multi-sensing technologies’ integrated applications, etc.
   This Special Issue covers the latest developments and applications of advanced non-destructive sensing technologies and intelligence systems for quality and safety evaluation of agricultural products, with relevant areas including but not limited to:

  1. Assessment of external and internal quality attributes;
  2. Safety attribute detection of agricultural products;
  3. Development of novel algorithms and models;
  4. Spectral and image data processing and analysis methods;
  5. Design and development of advanced sensing system;
  6. Intelligent sorting systems and robots for agricultural product detection;
  7. Multi-sensor fusion and integration applications;
  8. Online agricultural product grading and sorting.

Dr. Jiangbo Li
Dr. Baohua Zhang
Prof. Dr. Zhiming Guo
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. Agriculture 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 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

  • agro-products quality and safety
  • nondestructive sensing technique
  • intelligence system
  • online detection and sorting
  • agricultural robot

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Published Papers (3 papers)

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Research

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15 pages, 1977 KiB  
Article
Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model
by Baishao Zhan, Peng Li, Ming Li, Wei Luo and Hailiang Zhang
Agriculture 2023, 13(8), 1491; https://doi.org/10.3390/agriculture13081491 - 27 Jul 2023
Cited by 1 | Viewed by 1282
Abstract
The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of [...] Read more.
The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of sugars, organic acids, amino acids, esters, alcohols, phenols, flavonoids, and other compounds, and different groups within these compounds have different characteristic absorption peaks corresponding to different characteristic wavelengths. Traditional methods such as genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) models used for screening characteristic wavelengths are mainly based on statistical methods, and characteristic wavelengths are selected by finding the wavelengths related to the changes in the concentration of the target analytes. By ignoring the molecular structure and chemical properties of the target analytes and disregarding the influence of the groups of the compounds in the target analytes on the spectral characteristics, wavelengths that are not related to the target analytes may be selected, thus affecting the accuracy of the analytical results. In this paper, a partial least squares (PLS) model was established based on the characteristic wavelengths of CARS, GA, and LASSO algorithms, and the best least absolute shrinkage and selection operator (LASSO) was selected and compared with the characteristic wavelengths selected by group weighted fusion (GWF). The LASSO regression was validated by 10-fold cross-validation to select the appropriate regularization parameter, and the 33 characteristic wavelengths correlated with the SSC of pears were selected in the full spectral range, and the 9 characteristic wavelengths corresponding to the group response were weighted and fused and input into the PLS regression model. Using an established model, the coefficient of determination (R2) and the root mean square error (RMSE) of the calibration set were 0.992 and 0.177%, respectively, and the R2 and RMSE of the test set were 0.998 and 0.128%, respectively. The R2 of our LASSO–GWF–PLS prediction model was improved from 0.975 to 0.998, indicating that the LASSO–GWF–PLS method has very good prediction ability for detection of SSC in pears. Full article
(This article belongs to the Special Issue Nondestructive Sensing Techniques and Intelligence Systems for Agricultural Product Detection)
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13 pages, 2248 KiB  
Article
Development of a General Prediction Model of Moisture Content in Maize Seeds Based on LW-NIR Hyperspectral Imaging
by Zheli Wang, Jiangbo Li, Chi Zhang and Shuxiang Fan
Agriculture 2023, 13(2), 359; https://doi.org/10.3390/agriculture13020359 - 1 Feb 2023
Cited by 8 | Viewed by 2807
Abstract
Moisture content (MC) is one of the important indexes to evaluate maize seed quality. Its accurate prediction is very challenging. In this study, the long-wave near-infrared hyperspectral imaging (LW-NIR-HSI) system was used, and the embryo side (S1) and endosperm side (S2) [...] Read more.
Moisture content (MC) is one of the important indexes to evaluate maize seed quality. Its accurate prediction is very challenging. In this study, the long-wave near-infrared hyperspectral imaging (LW-NIR-HSI) system was used, and the embryo side (S1) and endosperm side (S2) spectra of each maize seed were extracted, as well as the average spectrum (S3) of both being calculated. The partial least square regression (PLSR) and least-squares support vector machine (LS-SVM) models were established. The uninformative variable elimination (UVE) and successive projections algorithm (SPA) were employed to reduce the complexity of the models. The results indicated that the S3-UVE-SPA-PLSR and S3-UVE-SPA-LS-SVM models achieved the best prediction accuracy with an RMSEP of 1.22% and 1.20%, respectively. Furthermore, the combination (S1+S2) of S1 and S2 was also used to establish the prediction models to obtain a general model. The results indicated that the S1+S2-UVE-SPA-LS-SVM model was more valuable with Rpre of 0.91 and RMSEP of 1.32% for MC prediction. This model can decrease the influence of different input spectra (i.e., S1 or S2) on prediction performance. The overall study indicated that LW-HSI technology combined with the general model could realize the non-destructive and stable prediction of MC in maize seeds. Full article
(This article belongs to the Special Issue Nondestructive Sensing Techniques and Intelligence Systems for Agricultural Product Detection)
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Review

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25 pages, 4346 KiB  
Review
Pomegranate Quality Evaluation Using Non-Destructive Approaches: A Review
by Emmanuel Ekene Okere, Ebrahiema Arendse, Alemayehu Ambaw Tsige, Willem Jacobus Perold and Umezuruike Linus Opara
Agriculture 2022, 12(12), 2034; https://doi.org/10.3390/agriculture12122034 - 28 Nov 2022
Cited by 10 | Viewed by 4241
Abstract
Pomegranate (Punica granatum L.) is one of the most healthful and popular fruits in the world. The increasing demand for pomegranate has resulted in it being processed into different food products and food supplements. Researchers over the years have shown interest in [...] Read more.
Pomegranate (Punica granatum L.) is one of the most healthful and popular fruits in the world. The increasing demand for pomegranate has resulted in it being processed into different food products and food supplements. Researchers over the years have shown interest in exploring non-destructive techniques as alternative approaches for quality assessment of the harvest at the on-farm point to the retail level. The approaches of non-destructive techniques are more efficient, inexpensive, faster and yield more accurate results. This paper provides a comprehensive review of recent applications of non-destructive technology for the quality evaluation of pomegranate fruit. Future trends and challenges of using non-destructive techniques for quality evaluation are highlighted in this review paper. Some of the highlighted techniques include computer vision, imaging-based approaches, spectroscopy-based approaches, the electronic nose and the hyperspectral imaging technique. Our findings show that most of the applications are focused on the grading of pomegranate fruit using machine vision systems and the electronic nose. Measurements of total soluble solids (TSS), titratable acidity (TA) and pH as well as other phytochemical quality attributes have also been reported. Value-added products of pomegranate fruit such as fresh-cut and dried arils, pomegranate juice and pomegranate seed oil have been non-destructively investigated for their numerous quality attributes. This information is expected to be useful not only for those in the grower/processing industries but also for other agro-food commodities. Full article
(This article belongs to the Special Issue Nondestructive Sensing Techniques and Intelligence Systems for Agricultural Product Detection)
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