IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i5p494-d1599398.html
   My bibliography  Save this article

Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management

Author

Listed:
  • Magdalena Piekutowska

    (Department of Botany and Nature Protection, Institute of Biology, Pomeranian University in Słupsk, 22b Arciszewskiego St., 76-200 Słupsk, Poland)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Sebastian Kujawa

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Tomasz Wojciechowski

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

Combining machine learning algorithms with Earth observations has great potential in the context of crop monitoring and management, which is essential in the face of global challenges related to food security and climate change [...]

Suggested Citation

  • Magdalena Piekutowska & Gniewko Niedbała & Sebastian Kujawa & Tomasz Wojciechowski, 2025. "Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management," Agriculture, MDPI, vol. 15(5), pages 1-3, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:494-:d:1599398
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/5/494/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/5/494/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sana Parez & Naqqash Dilshad & Jong Weon Lee, 2025. "A Channel Attention-Driven Optimized CNN for Efficient Early Detection of Plant Diseases in Resource Constrained Environment," Agriculture, MDPI, vol. 15(2), pages 1-17, January.
    2. Yue Yu & Qi Zhou & Hao Wang & Ke Lv & Lijuan Zhang & Jian Li & Dongming Li, 2024. "LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8," Agriculture, MDPI, vol. 14(8), pages 1-24, August.
    3. Diana-Carmen Rodríguez-Lira & Diana-Margarita Córdova-Esparza & José M. Álvarez-Alvarado & Juan Terven & Julio-Alejandro Romero-González & Juvenal Rodríguez-Reséndiz, 2024. "Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review," Agriculture, MDPI, vol. 14(12), pages 1-30, November.
    4. Gniewko Niedbała & Danuta Kurasiak-Popowska & Magdalena Piekutowska & Tomasz Wojciechowski & Michał Kwiatek & Jerzy Nawracała, 2022. "Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean ( Glycine max [L.] Merrill) Cultivar Augusta," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    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. Magdalena Piekutowska & Gniewko Niedbała, 2025. "Review of Methods and Models for Potato Yield Prediction," Agriculture, MDPI, vol. 15(4), pages 1-31, February.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    3. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    4. Jarosław Kurek & Gniewko Niedbała & Tomasz Wojciechowski & Bartosz Świderski & Izabella Antoniuk & Magdalena Piekutowska & Michał Kruk & Krzysztof Bobran, 2023. "Prediction of Potato ( Solanum tuberosum L.) Yield Based on Machine Learning Methods," Agriculture, MDPI, vol. 13(12), pages 1-25, December.
    5. Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    6. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    7. Kaiqiang Ye & Gang Hu & Zijie Tong & Youlin Xu & Jiaqiang Zheng, 2025. "Key Intelligent Pesticide Prescription Spraying Technologies for the Control of Pests, Diseases, and Weeds: A Review," Agriculture, MDPI, vol. 15(1), pages 1-37, January.

    More about this item

    Keywords

    n/a;

    Statistics

    Access and download statistics

    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:jagris:v:15:y:2025:i:5:p:494-:d:1599398. 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.