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Agriculture
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Agricultural Products Processing and Quality Detection
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Agricultural Products Processing and 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 September 2024 | Viewed by 5522

Special Issue Editors

Dr. Ziliang Liu

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Guest Editor
College of Engineering, China Agricultural University, Beijing 100107, China
Interests: agricultural products; processing technologies; artificial intelligence; machine learning
Dr. Jun Wang

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Guest Editor
College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
Interests: fruit processing; biologically active substance; quality detection; food safety
Dr. Lizhen Deng

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Guest Editor
State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China
Interests: fruits and vegetables; processing; food safety; quality analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, sustainable food supply for humans has been facing various challenges. In particular, the COVID-19 pandemic has aggravated the difficulty for people to obtain foods. The provision of nutritious and perishable fresh agricultural products including fruits, vegetables, seafood, and meat has also been deeply affected. On the other hand, multiple external factors, such as microorganisms, processing methods, and storage environments, have posed a threat to food quality and safety. Therefore, novel processing and quality detection technologies are urgently needed to sustainably provide high-quality and safe food products. The objective of this Special Issue is to collate a series of original research and review papers on the processing and quality detection technology applied in agricultural production, including but not limited to nutrition, quality assurance, bioactive compounds, and on-line monitoring.

Dr. Ziliang Liu
Dr. Jun Wang
Dr. Lizhen Deng
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

  • agricultural products
  • processing technologies
  • quality detection
  • food safety

Published Papers (6 papers)

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Research

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17 pages, 3821 KiB  
Article
Effects of Different Natural Drying Methods on Drying Characteristics and Quality of Diaogan apricots
by Qiaonan Yang, Can Hu, Jie Li, Hongwei Xiao, Wenwen Jia, Xufeng Wang, Xiangjuan Liu, Ziya Tang, Bingzhou Chen, Xiaokang Yi and Xibing Li
Agriculture 2024, 14(5), 660; https://doi.org/10.3390/agriculture14050660 (registering DOI) - 24 Apr 2024
Viewed by 235
Abstract
Drying is one of the best methods to preserve the quality of fresh fruits and prolong their shelf life. This study focuses on Prunus armeniaca L. cv. ‘Diaogan’ (commonly known as Diaogan apricot) sourced from Xinjiang, China to explore the impact of [...] Read more.
Drying is one of the best methods to preserve the quality of fresh fruits and prolong their shelf life. This study focuses on Prunus armeniaca L. cv. ‘Diaogan’ (commonly known as Diaogan apricot) sourced from Xinjiang, China to explore the impact of two natural drying methods (shade drying and open-air drying in the rocky desert) on the drying kinetics, color, textural characteristics, microstructure, chemical properties, and antioxidant capacity of Diaogan apricots. The experimental results indicate that throughout the natural drying process, the time required for open-air drying in the rocky desert was reduced by 26.47% compared to shade drying. The L*, a*, and b* values of the shade- and ventilation-dried Diaogan apricots were higher than those sun-dried in the rocky desert, exhibiting a lower color difference (ΔE) than apricots dried through rocky desert sun drying. Specifically, the ΔE for shade-dried Diaogan apricots was 19.66 ± 0.24. The Diaogan apricots dried in the rocky desert exhibited greater hardness, lower elasticity, stronger adhesiveness, and higher chewiness compared to those dried in the shade, with the hardness, adhesiveness, and chewiness being, respectively, 14.71%, 18.89%, and 35.79% higher. Scanning electron microscopy (SEM) observations revealed that the high temperatures experienced during open-air drying in the rocky desert caused rapid dehydration of the Diaogan apricot’s skin, leading to clogging and crust formation in the flesh pores, along with deformation or tearing of the tissue structure, ultimately resulting in poor rehydration ability. After drying, there was a significant increase in the soluble solids in the Diaogan apricots, whereas titratable acidity, total phenols, ascorbic acid, and antioxidant capacity were significantly decreased (p < 0.05). In summary, the quality of dried Diaogan apricots post-drying is dependent on the natural drying method employed, with shade drying resulting in superior quality of Diaogan apricots compared to open-air drying in the rocky desert. This study offers fundamental data and serves as a theoretical reference for the industrialized production of apricots. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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21 pages, 6580 KiB  
Article
Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques
by Zimei Zhang, Jianwei Xiao, Wenjie Wang, Magdalena Zielinska, Shanyu Wang, Ziliang Liu and Zhian Zheng
Agriculture 2024, 14(3), 507; https://doi.org/10.3390/agriculture14030507 - 21 Mar 2024
Viewed by 778
Abstract
Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for [...] Read more.
Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator’s observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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14 pages, 2499 KiB  
Article
ALIKE-APPLE: A Lightweight Method for the Detection and Description of Minute and Similar Feature Points in Apples
by Xinyao Huang, Tao Xu, Xiaomin Zhang, Yihang Zhu, Zheyuan Wu, Xufeng Xu, Yuan Gao, Yafei Wang and Xiuqin Rao
Agriculture 2024, 14(3), 339; https://doi.org/10.3390/agriculture14030339 - 21 Feb 2024
Viewed by 599
Abstract
Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists [...] Read more.
Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists of four industrial cameras placed around the apple at different angles and one camera placed on top. Following the image acquisition through the system, synthetic image pairs—both before and after transformation—were generated as the input dataset. This generation process involved each image being subjected to random transformations. Through learning to extract more distinctive and descriptive features, the deep learning-based keypoint detection method surpasses traditional techniques by broadening the application range and enhancing detection accuracy. Therefore, a lightweight network called ALIKE-APPLE was proposed for surface feature point detection. The baseline model for ALIKE-APPLE is ALIKE, upon which improvements have been made to the image feature encoder and feature aggregation modules. It comprises an Improved Convolutional Attention Module (ICBAM) and a Boosting Resolution Sampling Module (BRSM). The proposed ICBAM replaced max pooling in the original image feature encoder for downsampling. It enhanced the feature fusion capability of the model by utilizing spatial contextual information and learning region associations in the image. The proposed BRSM replaced the bilinear interpolation in the original feature aggregator for upsampling, overcoming the apple side image’s geometric distortion and effectively preserving the texture details and edge information. The model size was shrunk by optimizing the number of downsampling operations from the image encoder of the original model. The experimental results showed that the average number of observed keypoints and the average matching accuracy were improved by 166.41% and 37.07%, respectively, compared with the baseline model. The feature detection model of ALIKE-APPLE was found to perform better than the optimal SuperPoint. The feature point distribution of ALIKE-APPLE showed an improvement of 10.29% in average standard deviation (Std), 8.62% in average coefficient of variation (CV), and 156.12% in average feature point density (AFPD). Moreover, the mean matching accuracy (MMA) of ALIKE-APPLE improved by 125.97%. Thus, ALIKE-APPLE boasts a more consistent allocation of feature points and greater precision in matching. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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23 pages, 9601 KiB  
Article
Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test
by Haipeng Lin, Xuefeng Song, Fei Dai, Fengwei Zhang, Qiang Xie and Huhu Chen
Agriculture 2024, 14(2), 224; https://doi.org/10.3390/agriculture14020224 - 30 Jan 2024
Viewed by 871
Abstract
Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we developed machine learning models (MLMs) to predict the [...] Read more.
Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we developed machine learning models (MLMs) to predict the hardness of Jinsui No.4 maize seeds. The input variables of the MLM were loading speed, loading depth, and different types of indenters, and the output variable was the slope of the linear segment. Using the Latin square design, 100 datasets were generated. Four different types of MLMs, a genetic algorithm (GA), support vector machine (SVM), random forest (RF), and long short-term memory network (LSTM), were used for our data analysis, respectively. The result indicated that the GA model had a high accuracy in predicting hardness values, the R2 of the GA model training set and testing set reached 0.98402 and 0.92761, respectively, while the RMSEs were 1.4308 and 2.8441, respectively. The difference between the predicted values and the actual values obtained by the model is relatively small. Furthermore, in order to investigate the relationship between hardness and morphology after compression, scanning electron microscopy was used to observe the morphology of the maize grains. The result showed that the more complex the shape of the indenter, the more obvious the destruction to the internal polysaccharides and starch in the grain, and the number of surface cracks also significantly increases. The results of this study emphasize the potential of MLMs in determining the hardness of agricultural cereal grains, leading to improved industrial processing efficiency and cost savings. Additionally, combining grain hardness prediction models with the operating mechanisms of industry machinery would provide valuable references and a basis for the parameterization of seed grain processing machinery. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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24 pages, 3630 KiB  
Article
Physical–Chemical and Metataxonomic Characterization of the Microbial Communities Present during the Fermentation of Three Varieties of Coffee from Colombia and Their Sensory Qualities
by Laura Holguín-Sterling, Bertilda Pedraza-Claros, Rosangela Pérez-Salinas, Aristófeles Ortiz, Lucio Navarro-Escalante and Carmenza E. Góngora
Agriculture 2023, 13(10), 1980; https://doi.org/10.3390/agriculture13101980 - 12 Oct 2023
Cited by 1 | Viewed by 1027
Abstract
The microbial composition and physical-chemical characteristics were studied during the coffee fermentation of three Coffea arabica L. varieties, Var. Tabi, Var. Castillo General® and Var. Colombia. Mucilage and washed coffee seeds samples were collected at different stages of fermentation. Mucilage microbiology characterization [...] Read more.
The microbial composition and physical-chemical characteristics were studied during the coffee fermentation of three Coffea arabica L. varieties, Var. Tabi, Var. Castillo General® and Var. Colombia. Mucilage and washed coffee seeds samples were collected at different stages of fermentation. Mucilage microbiology characterization and metataxonomic analysis were performed using 16S rDNA sequencing to determine bacterial diversity and ITS sequencing for fungal diversity. Additionally, the microorganisms were isolated into pure cultures. The molecular diversity analyses showed similarities in microorganisms present during the fermentation of Var. Castillo General and Var. Colombia, which are genetically closely related; mixed-acid bacteria (Enterobacteriaceae, Tatumella sp.) and lactic acid bacteria (Leuconostoc sp., Weissella sp. and Lactobacillaceae) were common and predominant, while in Var. Tabi, acetic acid bacteria (Gluconobacter sp. and Acetobacter sp.) and Leuconostoc sp. were predominant. At the end of the fermentation period, the fungi Saccharomycodaceae, Pichia and Wickerhamomyces were found in Var. Castillo General and Var. Colombia, while in Var. Tabi, Saccharomycodaceae, Pichia and Candida were recorded. Sensory analyses of the coffee beverages were carried out (SCA methodology) for all samples. Var. Tabi had the highest SCA score, between 82.7 and 83.2, while for Var. Colombia, the score ranged between 82.1 and 82.5. These three coffee varieties showed potential for the production of specialty coffees influenced by spontaneous fermentation processes that depend on microbial consortia rather than a single microorganism. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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Review

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30 pages, 3540 KiB  
Review
Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest
by Dágila Melo Rodrigues, Paulo Carteri Coradi, Newiton da Silva Timm, Michele Fornari, Paulo Grellmann, Telmo Jorge Carneiro Amado, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio and José Luís Trevizan Chiomento
Agriculture 2024, 14(1), 161; https://doi.org/10.3390/agriculture14010161 - 22 Jan 2024
Viewed by 1360
Abstract
In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, we outline how [...] Read more.
In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, we outline how remote sensing tools can support a range of agricultural processes from field to storage through crop yield estimation, grain quality monitoring, storage unit identification and characterization, and production process planning. The use of sensors in the field and post-harvest processes allows for accurate real-time monitoring of operations and grain quality, enabling decision-making supported by computer tools such as the Internet of Things (IoT) and artificial intelligence algorithms. This way, grain producers can get ahead, track and reduce losses, and maintain grain quality from field to consumer. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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