Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images

Muhammad Riaz Hasib Hossain () and Muhammad Ashad Kabir
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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

Agriculture, 2023, vol. 13, issue 3, 1-25

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.

Keywords: soil moisture; image processing; smartphone; machine learning; deep learning; prediction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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