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Applied Sciences
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Detection of Agrochemical Residues in Agriculture
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Detection of Agrochemical Residues in Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 2335

Special Issue Editors

Dr. Hyun Ho Noh

E-Mail Website
Guest Editor
Residual Agrochemical Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration, Wanju, Republic of Korea
Interests: pesticide residue; mass spectrometry; drone application
Dr. Yongho Shin

E-Mail Website
Guest Editor
Department of Applied Biology, Dong-A University, Busan 49315, Republic of Korea
Interests: pesticide; multiresidues; edible insects; mealworms; LC-MS-MS; QuEChERS; acetonitrile-hexane partitioning

Special Issue Information

Dear Colleagues,

As a Special Issue Editor for Applied Sciences, I am pleased to invite you to submit your research to 'Detection of Agrochemical Residues in Agriculture'. The core focus of this Special Issue is the application of analytical methods for pesticides and organic compounds in agriculture, with an emphasis on mass spectrometry and residue testing. Not all manuscripts need to present experimental results; we also welcome review articles and those using meta-analyses.

This represents an excellent opportunity for you to apply your passion and expertise to creating outstanding content. We will collaborate, share innovative ideas, and work together to develop this Special Issue.

We are confident that your involvement will lead to the success of this Special Issue editorial project.

Once again, we welcome your participation in the Special Issue 'Detection of Agrochemical Residues in Agriculture' of Applied Sciences journal and look forward to embarking on this special journey. Please do not hesitate to reach out with any questions or comments.

Dr. Hyun Ho Noh
Dr. Yongho Shin
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • mass spectrometry 
  • chromatography
  • pesticides residue analysis

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Published Papers (3 papers)

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Research

15 pages, 2351 KiB  
Article
Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning
by Adarsh Basavaraju, Edwin Davidson, Giulio Diracca, Chen Chen and Swadeshmukul Santra
Appl. Sci. 2024, 14(22), 10087; https://doi.org/10.3390/app142210087 - 5 Nov 2024
Viewed by 568
Abstract
Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in [...] Read more.
Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse. Full article
(This article belongs to the Special Issue Detection of Agrochemical Residues in Agriculture)
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11 pages, 12694 KiB  
Article
Simultaneous Determination of Glyphosate and 13 Multiclass Pesticides in Agricultural Soil by Direct-Immersion SPME Followed by Solid–Liquid Extraction
by João Brinco, Paula Guedes, Marco Gomes da Silva, Eduardo P. Mateus and Alexandra B. Ribeiro
Appl. Sci. 2024, 14(19), 8584; https://doi.org/10.3390/app14198584 - 24 Sep 2024
Viewed by 717
Abstract
A new method is presented for the simultaneous determination of 13 multiclass pesticides along with glyphosate. The multiclass pesticides were extracted by creating a soil slurry with 2% ethanol in water (v/v), and then, applying direct-immersion solid-phase microextraction (DI-SPME) [...] Read more.
A new method is presented for the simultaneous determination of 13 multiclass pesticides along with glyphosate. The multiclass pesticides were extracted by creating a soil slurry with 2% ethanol in water (v/v), and then, applying direct-immersion solid-phase microextraction (DI-SPME) with a new type of semi-disposable SPME fiber configuration called LC-Tips. The fibers were then retroextracted to ethanol, and aqueous ammonia was added to the slurry to extract glyphosate. Derivatization of the glyphosate extract was accomplished with a mixture of trifluoroacetic anhydride and trifluoroethanol, after which the reaction mixture was dried and resuspended with the SPME ethanol extract. To this, a mixture of analyte protectants was added, and it was analyzed by GC-MS/MS in multiple-reaction-monitoring mode. All analytes showed a coefficient of determination greater than 0.95 in the 0.1–100 µg/kg calibrated range, and the limits of detection were between 0.1 and 1 µg/kg, except for glyphosate, which was 0.01 µg/kg. The method shows relatively high replicate relative standard deviation (as much as 37% for five extractions at 20 µg/kg), but the isotopically labeled internal standard was effective at mitigating this effect for some analytes. Full article
(This article belongs to the Special Issue Detection of Agrochemical Residues in Agriculture)
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12 pages, 1402 KiB  
Article
Enhancement of Tricyclazole Analysis Efficiency in Rice Samples Using an Improved QuEChERS and Its Application in Residue: A Study from Unmanned Arial Spraying
by Ye-Jin Lee, So-Hee Kim, Hye-Ran Eun, Su-Min Kim, Mun-Ju Jeong, Jae-Woon Baek, Yoon-Hee Lee, Hyun Ho Noh and Yongho Shin
Appl. Sci. 2024, 14(13), 5607; https://doi.org/10.3390/app14135607 - 27 Jun 2024
Cited by 3 | Viewed by 669
Abstract
Enhancements to the analytical method for the determination of tricyclazole in rice samples have been applied to monitor residues during unmanned aerial spraying. The acetonitrile extraction technique QuEChERS was improved by the incorporation of ethyl acetate and 0.1% formic acid, which significantly elevated [...] Read more.
Enhancements to the analytical method for the determination of tricyclazole in rice samples have been applied to monitor residues during unmanned aerial spraying. The acetonitrile extraction technique QuEChERS was improved by the incorporation of ethyl acetate and 0.1% formic acid, which significantly elevated the recovery rates. Furthermore, the purification process was refined by integrating both primary–secondary amine (PSA) and C18 in the dSPE method, achieving a substantial improvement in reducing matrix effects (MEs) and increasing recovery efficiency. The optimized method demonstrated an impressive % ME value at −3.1%, with a limit of quantitation (LOQ) established at 0.01 mg/kg, and recovery rates between 94.7 and 95.6% at 0.01, 0.1, and 2 mg/kg. Using two types of adjuvants (stickers) during multi-copter spraying markedly improved the initial tricyclazole deposition on rice panicles, with residue levels initially increasing from 0.35 mg/kg to between 0.68 and 1.60 mg/kg. Residues in hulled rice at harvest (10 days post-application) remained well below the maximum residue limit (MRL) of 0.7 mg/kg, ranging from 0.02 to 0.11 mg/kg, thus affirming the safety and efficacy of adjuvants in residue management. Full article
(This article belongs to the Special Issue Detection of Agrochemical Residues in Agriculture)
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