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Agriculture
Special Issues
Sensing and Imaging for Quality and Safety of Agricultural Products
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Sensing and Imaging for Quality and Safety of Agricultural Products

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 1680

Special Issue Editors

Prof. Dr. Wei Wang

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: hyperspectral imaging; machine vision; near infrared spectroscopy; nondestrctive detection; sorting; instruments and equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Seung-Chul Yoon

E-Mail Website
Guest Editor
USDA-ARS Quality & Safety Assessment Research Unit, Athens, GA, USA
Interests: hyperspectral imaging; artificial intelligence; deep learning; real-time machine vision; non-destructive sensing of agricultural and food products for safety and quality assessment; big image data
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoyu Tian

E-Mail Website
Guest Editor
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China
Interests: hyperspectral imaging; machine vision; colorimetric sensing; intelligent sensory evaluation of agricultural products and food quality

Special Issue Information

Dear Colleagues,

The quality and safety of agricultural products has become a social issue of widespread concern. Modern sensing technology is developing towards online, in situ, non-destructive, real-time and intuitive visualization, and dynamic monitoring. These emerging technologies, such as any type of optical, acoustic, spectral, electromagnetic, biological, and electrochemical sensors, as well as various advanced imaging technologies, such as thermal infrared imaging technology, visible light machine vision technology, fluorescence, near-infrared, infrared spectral imaging technology, and laser, microwave, ultrasonic, and nuclear magnetic resonance imaging technology, will greatly promote the rapid development of agricultural products and food quality and safety inspection and detection, as well as process monitoring, to better serve human life and health.

This Special Issue is dedicated to collating original papers related to sensing and imaging technology widely used in agricultural products, as well as review papers on the latest international progress, including but not limited to quality assurance, nutrition and deterioration monitoring of grains, oils, fruits, and vegetables, poultry eggs, meat, aquatic products and all kinds of manufactured products, at any stages of picking, harvesting, sorting, processing, transportation, storage, etc. In addition, relevant interdisciplinary technical basic and applied research is encouraged to be submitted.

Prof. Dr. Wei Wang
Dr. Seung-Chul Yoon
Dr. Xiaoyu Tian
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

  • sensors
  • imaging
  • quality and safety
  • agricultural products

Published Papers (2 papers)

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Research

18 pages, 5394 KiB  
Article
Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing
by Yuwen Li, Wei Wang, Xiaohuan Guo, Xiaorong Wang, Yizhe Liu and Daren Wang
Agriculture 2024, 14(4), 624; https://doi.org/10.3390/agriculture14040624 - 17 Apr 2024
Viewed by 320
Abstract
To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look [...] Read more.
To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look Once version 7 (YOLOv7) object detection algorithm and Red Green Blue-Depth (RGB-D) sensing. Modifications to the YOLOv7 model include the integration of more efficient modules, incorporation of attention mechanisms, elimination of superfluous feature layers, and the addition of layers dedicated to the detection of smaller targets. These modifications have culminated in a lightweight and improved YOLOv7 network model. The number of parameters is only 40.3% of that of the original model. The calculation amount is reduced by 41.8% and the model size by 59.2%. The recognition speed and accuracy are also both improved. The frame rate of model recognition is increased by 19.3%, the accuracy of model recognition reaches 98.8%, and mAP@0.95 reaches 96.8%. In addition, we have developed a method for locating strawberry picking points based on strawberry geometry. The test results demonstrated that the average positioning success rate and average positioning time were 90.8% and 76 ms, respectively. The picking robot in the laboratory utilized the recognition and positioning method proposed in this paper. The error of hand–eye calibration is less than 5.5 mm on the X-axis, less than 1.6 mm on the Y-axis, and less than 2.7 mm on the Z-axis, which meets the requirements of picking accuracy. The success rate of the picking experiment was about 90.8%, and the average execution time for picking each strawberry was 7.5 s. In summary, the recognition and positioning method proposed in this paper provides a more effective method for automatically picking elevated-substrate strawberries. Full article
(This article belongs to the Special Issue Sensing and Imaging for Quality and Safety of Agricultural Products)
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14 pages, 4944 KiB  
Article
Origin Intelligent Identification of Angelica sinensis Using Machine Vision and Deep Learning
by Zimei Zhang, Jianwei Xiao, Shanyu Wang, Min Wu, Wenjie Wang, Ziliang Liu and Zhian Zheng
Agriculture 2023, 13(9), 1744; https://doi.org/10.3390/agriculture13091744 - 02 Sep 2023
Viewed by 931
Abstract
The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica [...] Read more.
The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis (A. sinensis) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis. Full article
(This article belongs to the Special Issue Sensing and Imaging for Quality and Safety of Agricultural Products)
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