IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i3p574-d1081637.html
   My bibliography  Save this article

Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images

Author

Listed:
  • Muhammad Riaz Hasib Hossain

    (School of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia)

  • Muhammad Ashad Kabir

    (School of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia
    Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, NSW 2678, Australia)

Abstract

Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R 2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R 2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.

Suggested Citation

  • Muhammad Riaz Hasib Hossain & Muhammad Ashad Kabir, 2023. "Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images," Agriculture, MDPI, vol. 13(3), pages 1-25, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:574-:d:1081637
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/3/574/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/3/574/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng, C.-L. & Shalabh, & Garg, G., 2014. "Coefficient of determination for multiple measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 137-152.
    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. Hammad Tahir & Muhammad Basit Khan & Nasir Shafiq & Dorin Radu & Marijana Hadzima Nyarko & Ahsan Waqar & Hamad R. Almujibah & Omrane Benjeddou, 2023. "Optimisation of Mechanical Characteristics of Alkali-Resistant Glass Fibre Concrete towards Sustainable Construction," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    2. Tomasz Chrulski & Mariusz Łaciak, 2021. "Analysis of Natural Gas Consumption Interdependence for Polish Industrial Consumers on the Basis of an Econometric Model," Energies, MDPI, vol. 14(22), pages 1-26, November.
    3. Cheng, C.-L. & Shalabh, & Garg, G., 2016. "Goodness of fit in restricted measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 101-116.
    4. Fausto André Valenzuela-Domínguez & Luis Alfonso Santa Cruz & Enrique A. Enríquez-Velásquez & Luis C. Félix-Herrán & Victor H. Benitez & Jorge de-J. Lozoya-Santos & Ricardo A. Ramírez-Mendoza, 2021. "Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico," Energies, MDPI, vol. 14(19), pages 1-37, October.
    5. Bresson Georges & Chaturvedi Anoop & Rahman Mohammad Arshad & Shalabh, 2021. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 75-97, May.
    6. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    7. Oana Vlăduţ & George Eduard Grigore & Dumitru Alexandru Bodislav & Gabriel Ilie Staicu & Raluca Iuliana Georgescu, 2024. "Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries," Energies, MDPI, vol. 17(1), pages 1-30, January.

    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:574-:d:1081637. 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.