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Evaluation of Quality and Safety of Agricultural Products by Nondestructive...
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Evaluation of Quality and Safety of Agricultural Products by Nondestructive Technologies

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 (10 August 2024) | Viewed by 12532

Special Issue Editors

Dr. Wei Lu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Interests: intelligent robots and artificial intelligence technology; intelligent sensing and Internet of Things; non-destructive testing technology
Prof. Dr. Zhao Zhang

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: crop phenotype; smart orchard; intelligent agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The quality and safety of agricultural products are directly related to their level of output and quality, and have a strong relationship with the national economy and population survival. Agricultural products are strategic resources able to ensure national food security and the supply of important agricultural products, and are the material basis for original innovations in agricultural science and technology and the development of modern agriculture.

This Special Issue aims to receive and present more excellent and innovative papers on the quality and safety of agricultural products. It mainly covers non-destructive testing technology for agricultural products, intelligent manufacturing, optical fields, etc. This Special Issue embraces studies on seeds, plants, roots, diseases, phenotypes, fruit, yields, soil, and food processing and product quality inspection, including, but not limited to, the application of spectroscopy, hyper-spectrum, photoacoustic spectroscopy, thermal infrared, spatial frequency domain imaging, terahertz, biosensors, and other advanced technologies combined with machine learning and AI. In addition, we encourage studies that are focused on highly innovative, practical, high-precision non-destructive testing and intelligent automation in the quality and safety testing of agricultural products, and have promising applications and promotion value in the agricultural field. All types of articles, such as original research, opinions, and reviews, are welcome.

Dr. Wei Lu
Prof. Dr. Zhao Zhang
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

  • nondestructive testing
  • phenotype
  • seed/seedling quality
  • disease/pest detection
  • yield prediction, soil detection
  • physiological parameter detection
  • food quality detection
  • biosensor
  • agricultural instruments

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Published Papers (5 papers)

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Research

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15 pages, 4142 KiB  
Article
Non-Destructive Seed Viability Assessment via Multispectral Imaging and Stacking Ensemble Learning
by Ye Rin Chu, Min Su Jo, Ga Eun Kim, Cho Hee Park, Dong Jun Lee, Sang Hoon Che and Chae Sun Na
Agriculture 2024, 14(10), 1679; https://doi.org/10.3390/agriculture14101679 - 26 Sep 2024
Viewed by 3463
Abstract
The tetrazolium (TZ) test is a reliable but destructive method for identifying viable seeds. In this study, a non-destructive seed viability analysis method for Allium ulleungense was developed using multispectral imaging and stacking ensemble learning. Using the Videometerlab 4, multispectral imaging data were [...] Read more.
The tetrazolium (TZ) test is a reliable but destructive method for identifying viable seeds. In this study, a non-destructive seed viability analysis method for Allium ulleungense was developed using multispectral imaging and stacking ensemble learning. Using the Videometerlab 4, multispectral imaging data were collected from 390 A. ulleungense seeds subjected to NaCl-accelerated aging treatments with three repetitions per treatment. Spectral values were obtained at 19 wavelengths (365–970 nm), and seed viability was determined using the TZ test. Next, 80% of spectral values were used to train Decision Tree, Random Forest, LightGBM, and XGBoost machine learning models, and 20% were used for testing. The models classified viable and non-viable seeds with an accuracy of 95–91% on the K-Fold value (n = 5) and 85–81% on the test data. A stacking ensemble model was developed using a Decision Tree as the meta-model, achieving an AUC of 0.93 and a test accuracy of 90%. Feature importance and SHAP value assessments identified 570, 645, and 940 nm wavelengths as critical for seed viability classification. These results demonstrate that machine learning-based spectral data analysis can be effectively used for seed viability assessment, potentially replacing the TZ test with a non-destructive method. Full article
(This article belongs to the Special Issue Evaluation of Quality and Safety of Agricultural Products by Nondestructive Technologies)
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15 pages, 4265 KiB  
Article
Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System
by Ebrahim Taghinezhad, Vali Rasooli Sharabiani, Mohammadali Shahiri, Abdolmajid Moinfar and Antoni Szumny
Agriculture 2023, 13(10), 1913; https://doi.org/10.3390/agriculture13101913 - 29 Sep 2023
Cited by 4 | Viewed by 1566
Abstract
This paper presents a comprehensive analysis of the application of visible–near-infrared (Vis/NIR) spectroscopy for the estimation of various chemical attributes of pear fruit. Specifically, the paper investigates how pH, titratable acidity (TA), soluble solids content (SSC), and Vitamin C change as the pear [...] Read more.
This paper presents a comprehensive analysis of the application of visible–near-infrared (Vis/NIR) spectroscopy for the estimation of various chemical attributes of pear fruit. Specifically, the paper investigates how pH, titratable acidity (TA), soluble solids content (SSC), and Vitamin C change as the pear undergoes different storage durations and temperatures. To obtain the most accurate prediction models, we applied a variety of pre-processing techniques to the acquired spectra. Notably, the combination of Savitzky-Golay (S.G.), Multiplicative Scatter Correction (MSC), and second derivatives (D2) emerged as the most effective method for predicting the fruit’s pH, with an impressive rp = 0.95 and SDR = 4.9. In contrast, combining S.G., MSC, and first derivatives (D1) yielded the most accurate predictions for TA, with a robust rp = 0.98 and SDR = 9.6. The research further delved into understanding how the storage period and temperature can significantly influence the pear fruit’s chemical properties. Our findings established that as the storage duration and temperature rise, the pH of the fruit also escalates, while TA sees a decline. The research further elucidates that prolonged storage periods and elevated temperatures lead to the pear fruit shedding its intrinsic qualities, resulting in a reduction in soluble solids and Vitamin C content. To summarize, this paper underscores the immense potential of Vis/NIR spectroscopy as a non-destructive and expedient tool for monitoring the chemical attributes of pear fruit during storage, especially when subjected to diverse temperature and time conditions. These insights not only add to the existing body of knowledge but also align with earlier research on how storage conditions can affect fruit quality. Full article
(This article belongs to the Special Issue Evaluation of Quality and Safety of Agricultural Products by Nondestructive Technologies)
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27 pages, 7433 KiB  
Article
Lettuce Plant Trace-Element-Deficiency Symptom Identification via Machine Vision Methods
by Jinzhu Lu, Kaiqian Peng, Qi Wang and Cong Sun
Agriculture 2023, 13(8), 1614; https://doi.org/10.3390/agriculture13081614 - 15 Aug 2023
Cited by 4 | Viewed by 2228
Abstract
Lettuce is one of the most widely planted leafy vegetables in plant factories. The lack of trace elements in nutrient solutions has caused huge losses to the lettuce industry. Non-obvious symptoms of trace element deficiency, the inconsistent size of the characteristic areas, and [...] Read more.
Lettuce is one of the most widely planted leafy vegetables in plant factories. The lack of trace elements in nutrient solutions has caused huge losses to the lettuce industry. Non-obvious symptoms of trace element deficiency, the inconsistent size of the characteristic areas, and the difficulty of extraction in different growth stages are three key problems affecting lettuce deficiency symptom identification. In this study, a batch of cream lettuce (lactuca sativa) was planted in the plant factory, and its nutrient elements were artificially controlled. We collected images of the lettuce at different growth stages, including all nutrient elements and three nutrient-deficient groups (potassium deficiency, calcium deficiency, and magnesium deficiency), and performed feature extraction analysis on images of different defects. We used traditional algorithms (k-nearest neighbor, support vector machine, random forest) and lightweight deep-learning models (ShuffleNet, SqueezeNet, andMobileNetV2) for classification, and we compared different feature extraction methods (texture features, color features, scale-invariant feature transform features). The experiment shows that, under the optimal feature extraction method (color), the random-forest recognition results are the best, with an accuracy rate of 97.6%, a precision rate of 97.9%, a recall rate of 97.4%, and an F1 score of 97.6%. The accuracies of all three deep-learning models exceed 99.5%, among which ShuffleNet is the best, with the accuracy, precision, recall, and F1 score above 99.8%. It also uses fewer floating-point operations per second and less time. The proposed method can quickly identify the trace elements lacking in lettuce, and it can provide technical support for the visual recognition of the disease patrol robot in the plant factory. Full article
(This article belongs to the Special Issue Evaluation of Quality and Safety of Agricultural Products by Nondestructive Technologies)
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18 pages, 3320 KiB  
Article
Vigor Detection for Naturally Aged Soybean Seeds Based on Polarized Hyperspectral Imaging Combined with Ensemble Learning Algorithm
by Qingying Hu, Wei Lu, Yuxin Guo, Wei He, Hui Luo and Yiming Deng
Agriculture 2023, 13(8), 1499; https://doi.org/10.3390/agriculture13081499 - 27 Jul 2023
Cited by 4 | Viewed by 1496
Abstract
To satisfy the increasing demand for soybeans, identifying and sorting high-vigor seeds before sowing is an effective way to improve the yield. Polarized hyperspectral imaging (PHI) technology is here proposed as a rapid, non-destructive method for detecting the vigor of naturally aged soybean [...] Read more.
To satisfy the increasing demand for soybeans, identifying and sorting high-vigor seeds before sowing is an effective way to improve the yield. Polarized hyperspectral imaging (PHI) technology is here proposed as a rapid, non-destructive method for detecting the vigor of naturally aged soybean seeds. First, the spectrum of 396.1–1044.1 nm was collected to automatically extract the region of interest (ROI). Then, first derivative (FD), Savitzky–Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) preprocessed hyperspectral and polarized hyperspectral data (0°, 45°, 90°, and 135°) for the soybean seeds was obtained. Finally, the seed vigor prediction model based on polarized hyperspectral components such as I, Q, and U was constructed, and partial least squares regression (PLSR), back-propagation neural network (BPNN), generalized regression neural network (GRNN), support vector regression (SVR), random forest (RF), and blending ensemble learning were applied for modeling analysis. The results showed that the prediction accuracy when using PHI was improved to 93.36%, higher than that for the hyperspectral technique, with a prediction accuracy up to 97.17%, 98.25%, and 97.55% when using the polarization component of I, Q, and U, respectively. Full article
(This article belongs to the Special Issue Evaluation of Quality and Safety of Agricultural Products by Nondestructive Technologies)
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Review

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23 pages, 2148 KiB  
Review
Haystack Fires in Australia: Causes and Considerations for Preventative Management
by Imtiaz F. Chowdhury, Joseph R. Moore and John C. Broster
Agriculture 2023, 13(12), 2238; https://doi.org/10.3390/agriculture13122238 - 4 Dec 2023
Viewed by 2758
Abstract
The spontaneous combustion of hay when stacked after baling is an issue frequently encountered by farmers in Australia and elsewhere. While there is a basic understanding of why this occurs the interactions of the many factors involved mean that there is still no [...] Read more.
The spontaneous combustion of hay when stacked after baling is an issue frequently encountered by farmers in Australia and elsewhere. While there is a basic understanding of why this occurs the interactions of the many factors involved mean that there is still no consistent methodology for its prevention. Recent technological advances in sensors and communications allow for the continual collection of quantitative data from hay bales or stacks for managers to utilize in their decision-making processes with regards to minimizing the risks of spontaneous combustion. This review discusses both the factors involved in the spontaneous combustion of haystacks and the types of sensors available for the monitoring of these factors. This includes advancements in sensor technologies and their practical applications in monitoring hay bale conditions. Full article
(This article belongs to the Special Issue Evaluation of Quality and Safety of Agricultural Products by Nondestructive Technologies)
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