IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i3p627-d1089327.html
   My bibliography  Save this article

Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania

Author

Listed:
  • Isakwisa Gaddy Tende

    (Department of Computer Studies, Dar es Salaam Institute of Technology, Dar es Salaam P.O. Box 2958, Tanzania)

  • Kentaro Aburada

    (Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan)

  • Hisaaki Yamaba

    (Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan)

  • Tetsuro Katayama

    (Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan)

  • Naonobu Okazaki

    (Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan)

Abstract

Prediction of crop yields is very helpful in ensuring food security, planning harvest management (storage, transport, and labor), and performing market planning. However, in Tanzania, where a majority of the population depends on crop farming as a primary economic activity, the digital tools for predicting crop yields are not yet available, especially at the grass-roots level. In this study, we developed and evaluated Maize Yield Prediction System (MYPS) that uses a short message service (SMS) and the Web to allow rural farmers (via SMS on mobile phones) and government officials (via Web browsers) to predict district-level end-of-season maize yields in Tanzania. The system uses LSTM (Long Short-Term Memory) deep learning models to forecast district-level season-end maize yields from remote sensing data (NDVI on the Terra MODIS satellite) and climate data [maximum temperature, minimum temperature, soil moisture, and precipitation (rainfall)]. The key findings reveal that our unimodal and bimodal deep learning models are very effective in predicting crop yields, achieving mean absolute percentage error (MAPE) scores of 3.656% and 6.648%, respectively, on test (unseen) data. This system will help rural farmers and the government in Tanzania make critical decisions to prevent hunger and plan better harvesting and marketing of crops.

Suggested Citation

  • Isakwisa Gaddy Tende & Kentaro Aburada & Hisaaki Yamaba & Tetsuro Katayama & Naonobu Okazaki, 2023. "Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:627-:d:1089327
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/3/627/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/3/627/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khadijeh Alibabaei & Pedro D. Gaspar & Tânia M. Lima, 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling," Energies, MDPI, vol. 14(11), pages 1-21, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    2. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.

    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. 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).
    2. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    3. Guilherme Jesus & Martim L. Aguiar & Pedro D. Gaspar, 2022. "Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants," Energies, MDPI, vol. 15(22), pages 1-20, November.
    4. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
    5. Eduardo Assunção & Pedro D. Gaspar & Khadijeh Alibabaei & Maria P. Simões & Hugo Proença & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application," Future Internet, MDPI, vol. 14(11), pages 1-12, November.
    6. Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
    7. Irtiqa Malik & Muneeb Ahmed & Yonis Gulzar & Sajad Hassan Baba & Mohammad Shuaib Mir & Arjumand Bano Soomro & Abid Sultan & Osman Elwasila, 2023. "Estimation of the Extent of the Vulnerability of Agriculture to Climate Change Using Analytical and Deep-Learning Methods: A Case Study in Jammu, Kashmir, and Ladakh," Sustainability, MDPI, vol. 15(14), pages 1-25, July.

    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:13:y:2023:i:3:p:627-:d:1089327. 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.