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Agronomy
Special Issues
Rapid Detection of Agricultural Products
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Rapid Detection of Agricultural Products

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 8900

Special Issue Editors

Prof. Dr. Wanneng Yang

E-Mail Website
Guest Editor
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: CT; plant phenomics
Special Issues, Collections and Topics in MDPI journals
Dr. Ruifang Zhai

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: crop phenomics and computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agricultural products are necessities of daily life. The quality and safety of agricultural products is currently a hot issue with the globalization of agricultural product trade and agricultural product supply chains. Various challenges including chemical pollution, microbial contamination and adulteration are faced during agriculture value chains covering cultivation, planting, harvest, post-production, industrial processing and even retail processes. Simultaneously, with increased expectations for agricultural products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in agricultural products continue to grow. Hence, new technologies (hyperspectral imaging, X-ray imaging, etc.) and new methodologies (machine learning, computer vision, etc.) are introduced and applied to meet demand for the rapid detection of agricultural products. In this respect, we encourage submissions describing state-of-the-art rapid detection techniques and applications in studies related to agricultural products, with a particular focus on these areas:

  1. Plant or food insect/disease classification and detection.
  2. In situ analysis of pesticide residues.
  3. Quality inspection for agricultural products.
  4. Rapid detection for chemical pollution in agricultural products.
  5. Microbial detection and identification methods in agricultural products.
  6. Machine learning and deep learning in the rapid detection of agricultural products.
  7. Plant phenotyping and applications in agricultural products.

Prof. Dr. Wanneng Yang
Prof. Dr. Ruifang Zhai
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. Agronomy 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

  • chemical pollution
  • pesticide residues
  • microbial contaminants
  • in situ inspection and analysis
  • insect classification and detection
  • big data
  • machine vision
  • deep learning
  • plant phenotyping

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Published Papers (4 papers)

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Research

18 pages, 10149 KiB  
Article
Hierarchical Detection of Gastrodia elata Based on Improved YOLOX
by Xingwei Duan, Yuhao Lin, Lixia Li, Fujie Zhang, Shanshan Li and Yuxin Liao
Agronomy 2023, 13(6), 1477; https://doi.org/10.3390/agronomy13061477 - 26 May 2023
Cited by 3 | Viewed by 1577
Abstract
Identifying the grade of Gastrodia elata in the market has low efficiency and accuracy. To address this issue, an I-YOLOX object detection algorithm based on deep learning and computer vision is proposed in this paper. First, six types of Gastrodia elata images of [...] Read more.
Identifying the grade of Gastrodia elata in the market has low efficiency and accuracy. To address this issue, an I-YOLOX object detection algorithm based on deep learning and computer vision is proposed in this paper. First, six types of Gastrodia elata images of different grades in the Gastrodia elata planting cooperative were collected for image enhancement and labeling as the model training dataset. Second, to improve feature information extraction, an ECA attention mechanism module was inserted between the backbone network CSPDarknet and the neck enhancement feature extraction network FPN in the YOLOX model. Then, the impact of the attention mechanism and application position on model improvement was investigated. Third, the 3 × 3 convolution in the neck enhancement feature extraction network FPN and the head network was replaced by depthwise separable convolution (DS Conv) to reduce the model size and computation amount. Finally, the EIoU loss function was used to predict boundary frame regression at the output prediction end to improve the convergence speed of the model. The experimental results indicated that compared with the original YOLOX model, the mean average precision of the improved I-YOLOX network model was increased by 4.86% (97.83%), the model computation was reduced by 5.422 M (reaching 3.518 M), the model size was reduced by 20.6 MB (reaching 13.7 MB), and the image frames detected per second increased by 3 (reaching 69). Compared with other target detection algorithms, the improved model outperformed Faster R-CNN, SSD-VGG, YOLOv3s, YOLOv4s, YOLOv5s, and YOLOv7 algorithms in terms of mean average precision, model size, computation amount, and frames per second. The lightweight model improved the detection accuracy and speed of different grades of Gastrodia elata and provided a theoretical basis for the development of online identification systems of different grades of Gastrodia elata in practical production. Full article
(This article belongs to the Special Issue Rapid Detection of Agricultural Products)
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10 pages, 2415 KiB  
Article
Effects of Fluctuating Thermal Regimes and Pesticides on Egg Hatching of a Natural Enemy Harmonia axyridis (Coleoptera Coccinellidae)
by Jingya Yu, Chong Li, Likun Dong, Runping Mao, Zhihua Wang, Zhangxin Pei and Letian Xu
Agronomy 2023, 13(6), 1470; https://doi.org/10.3390/agronomy13061470 - 26 May 2023
Viewed by 1304
Abstract
The harlequin ladybird, Harmonia axyridis, is a valuable asset in integrated pest management (IPM); however, issues related to low-temperature storage and transportation have resulted in low hatching rate, while the use of pesticides may lead to non-target effects against this natural enemy [...] Read more.
The harlequin ladybird, Harmonia axyridis, is a valuable asset in integrated pest management (IPM); however, issues related to low-temperature storage and transportation have resulted in low hatching rate, while the use of pesticides may lead to non-target effects against this natural enemy during field application. Fluctuating thermal regimes (FTR) have been shown to be beneficial during the low-temperature storage, and the type and concentration of insecticides used are crucial for field application of H. axyridis. Despite this, little research has been conducted on the effects of FTR on the hatching rate of ladybird eggs, and the impact of pesticides on their egg viability remains unclear. To address these gaps, we investigated the effects of different thermal temperatures, recovery frequencies (the number of changes in temperature conditions per unit time), and recovery durations (the duration of the treated temperature condition) on egg hatching under constant low-temperature conditions. We also examined the toxicity and safety of seven commonly used insecticides on egg hatching. Our results indicate that the temperature during FTR application did not significantly affect egg hatching, but the interaction between temperature and recovery frequency can significantly affect egg hatching. Moreover, the recovery frequency and recovery duration had a significant impact on hatching. Under specific conditions, the hatching rate of eggs subjected to FTR was similar to that of eggs stored at 25 °C. Furthermore, we found that matrine (a kind of alkaloid pesticide isolated from Sophora flavescens) had low toxicity to ladybird eggs and is a safe pesticide for use in conjunction with this natural enemy. The study provides valuable information on effectively managing H. axyridis by taking into account both storage temperature and pesticide exposure. Full article
(This article belongs to the Special Issue Rapid Detection of Agricultural Products)
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14 pages, 8017 KiB  
Article
InceptionV3-LSTM: A Deep Learning Net for the Intelligent Prediction of Rapeseed Harvest Time
by Shaojie Han, Jianxiao Liu, Guangsheng Zhou, Yechen Jin, Moran Zhang and Shengyong Xu
Agronomy 2022, 12(12), 3046; https://doi.org/10.3390/agronomy12123046 - 1 Dec 2022
Cited by 6 | Viewed by 1869
Abstract
Timely harvest can effectively guarantee the yield and quality of rapeseed. In order to change the artificial experience model in the monitoring of rapeseed harvest period, an intelligent prediction method of harvest period based on deep learning network was proposed. Three varieties of [...] Read more.
Timely harvest can effectively guarantee the yield and quality of rapeseed. In order to change the artificial experience model in the monitoring of rapeseed harvest period, an intelligent prediction method of harvest period based on deep learning network was proposed. Three varieties of field rapeseed in the harvest period were divided into 15 plots, and mobile phones were used to capture images of silique and stalk and manually measure the yield. The daily yield was divided into three grades of more than 90%, 70–90%, and less than 70%, according to the proportion of the maximum yield of varieties. The high-dimensional features of rapeseed canopy images were extracted using CNN networks in the HSV space that were significantly related to the maturity of the rapeseed, and the seven color features of rapeseed stalks were screened using random forests in the three color-spaces of RGB/HSV/YCbCr to form a canopy-stalk joint feature as input to the subsequent classifier. Considering that the rapeseed ripening process is a continuous time series, the LSTM network was used to establish the rapeseed yield classification prediction model. The experimental results showed that Inception v3 of the five CNN networks has the highest prediction accuracy. The recognition rate was 91% when only canopy image features were used, and the recognition rate using canopy-stalk combined features reached 96%. This method can accurately predict the yield level of rapeseed in the mature stage by only using a mobile phone to take a color image, and it is expected to become an intelligent tool for rapeseed production. Full article
(This article belongs to the Special Issue Rapid Detection of Agricultural Products)
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17 pages, 4018 KiB  
Article
3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images
by Yinghua Wang, Songtao Hu, He Ren, Wanneng Yang and Ruifang Zhai
Agronomy 2022, 12(8), 1865; https://doi.org/10.3390/agronomy12081865 - 8 Aug 2022
Cited by 9 | Viewed by 3134
Abstract
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on [...] Read more.
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a 3D phenotyping pipeline, 3DPhenoMVS, to calculate 17 phenotypic traits of tomato plants covering the whole life cycle. Among all the phenotypic traits, six of them were used for accuracy evaluation because the true values can be generated by manual measurements, and the results showed that the R2 values between the phenotypic traits and the manual ones ranged from 0.72 to 0.97. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during seven growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher biomass and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato and other plants, and the generalization test was also complemented on other six species, which demonstrated that the proposed pipeline will benefit plant breeding, cultivation research, and functional genomics in the future. Full article
(This article belongs to the Special Issue Rapid Detection of Agricultural Products)
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