IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i5p4120-d1080202.html
   My bibliography  Save this article

A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments

Author

Listed:
  • Qingchuan Zhang

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Zihan Li

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Wei Dong

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Siwei Wei

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Yingjie Liu

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Min Zuo

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

Abstract

Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major staple grains, as the target grains whose storage monitoring data cover more than 20 regions, and constructed a grain storage process quality change prediction model, which includes a FEDformer-based grain storage process quality change prediction model and a K-means++-based grain storage process quality change grading evaluation model. We select six factors affecting grain quality as input to achieve effective prediction of grain quality. Then, evaluation indexes were defined in this study, and a grading evaluation model of grain storage process quality was constructed using clustering model with the index prediction results and current values. The experimental results showed that the grain storage process quality change prediction model had the highest prediction accuracy and the lowest prediction error compared with other models.

Suggested Citation

  • Qingchuan Zhang & Zihan Li & Wei Dong & Siwei Wei & Yingjie Liu & Min Zuo, 2023. "A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4120-:d:1080202
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/5/4120/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/5/4120/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bingchun Liu & Ningbo Zhang & Lingli Wang & Xinming Zhang, 2022. "Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model," IJERPH, MDPI, vol. 19(11), pages 1-16, May.
    2. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    3. Qihui Xie & Yanan Xue, 2022. "The Prediction of Public Risk Perception by Internal Characteristics and External Environment: Machine Learning on Big Data," IJERPH, MDPI, vol. 19(15), pages 1-20, August.
    4. Yang Lu & Therese L. Williams, 2021. "Modeling analytics in COVID-19: prediction, prevention, control, and evaluation," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(3), pages 424-442, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Silva, J.F. & Santos, J.L. & Ribeiro, P.F. & Marta-Pedroso, C. & Magalhães, M.R. & Moreira, F., 2024. "A farming systems approach to assess synergies and trade-offs among ecosystem services," Ecosystem Services, Elsevier, vol. 65(C).
    2. Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).
    3. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    4. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    5. Helder Fraga & Teresa R. Freitas & Marco Moriondo & Daniel Molitor & João A. Santos, 2024. "Determining the Climatic Drivers for Wine Production in the Côa Region (Portugal) Using a Machine Learning Approach," Land, MDPI, vol. 13(6), pages 1-16, May.
    6. Florian Schierhorn & Max Hofmann & Taras Gagalyuk & Igor Ostapchuk & Daniel Müller, 2021. "Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages," Climatic Change, Springer, vol. 169(3), pages 1-19, December.
    7. Devkota, Mina & Yigezu, Yigezu Atnafe, 2020. "Explaining yield and gross margin gaps for sustainable intensification of the wheat-based systems in a Mediterranean climate," Agricultural Systems, Elsevier, vol. 185(C).
    8. Puyu Feng & Bin Wang & De Li Liu & Hongtao Xing & Fei Ji & Ian Macadam & Hongyan Ruan & Qiang Yu, 2018. "Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia," Climatic Change, Springer, vol. 147(3), pages 555-569, April.
    9. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    10. Li Fan & Shibo Fang & Jinlong Fan & Yan Wang & Linqing Zhan & Yongkun He, 2024. "Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    11. Martin Kuradusenge & Eric Hitimana & Damien Hanyurwimfura & Placide Rukundo & Kambombo Mtonga & Angelique Mukasine & Claudette Uwitonze & Jackson Ngabonziza & Angelique Uwamahoro, 2023. "Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize," Agriculture, MDPI, vol. 13(1), pages 1-19, January.
    12. Barlin O. Olivares & Andrés Vega & María A. Rueda Calderón & Edilberto Montenegro-Gracia & Miguel Araya-Almán & Edgloris Marys, 2022. "Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    13. Banda, Enid & Rafiei, Vahid & Kpodo, Josué & Nejadhashemi, A. Pouyan & Singh, Gurjeet & Das, Narendra N. & Kc, Rabin & Diallo, Amadiane, 2024. "Millet yield estimations in Senegal: Unveiling the power of regional water stress analysis and advanced predictive modeling," Agricultural Water Management, Elsevier, vol. 291(C).
    14. Keach Murakami & Seiji Shimoda & Yasuhiro Kominami & Manabu Nemoto & Satoshi Inoue, 2021. "Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-19, October.
    15. Xu Zhang & Guangsheng Chen & Lingxiao Cai & Hongbo Jiao & Jianwen Hua & Xifang Luo & Xinliang Wei, 2021. "Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    16. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2021. "Estimation of the Farm-Level Yield-Weather-Relation Using Machine Learning," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317075, German Association of Agricultural Economists (GEWISOLA).
    17. Timsina, Jagadish & Dutta, Sudarshan & Devkota, Krishna Prasad & Chakraborty, Somsubhra & Neupane, Ram Krishna & Bishta, Sudarshan & Amgain, Lal Prasad & Singh, Vinod K. & Islam, Saiful & Majumdar, Ka, 2021. "Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency," Agricultural Systems, Elsevier, vol. 192(C).
    18. Jaturong Som-ard & Savittri Ratanopad Suwanlee & Dusadee Pinasu & Surasak Keawsomsee & Kemin Kasa & Nattawut Seesanhao & Sarawut Ninsawat & Enrico Borgogno-Mondino & Filippo Sarvia, 2024. "Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms," Land, MDPI, vol. 13(9), pages 1-19, September.
    19. Li, Siyi & Wang, Bin & Feng, Puyu & Liu, De Li & Li, Linchao & Shi, Lijie & Yu, Qiang, 2022. "Assessing climate vulnerability of historical wheat yield in south-eastern Australia's wheat belt," Agricultural Systems, Elsevier, vol. 196(C).
    20. Kouame, Anselme K.K. & Bindraban, Prem S. & Kissiedu, Isaac N. & Atakora, Williams K. & El Mejahed, Khalil, 2023. "Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach," Agricultural Systems, Elsevier, vol. 209(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4120-:d:1080202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.