Soil organic matter prediction using smartphone-captured digital images: Use of reflectance image and image perturbation. (September 2021)

• Record Type: Journal Article
• Authors: Gorthi, Srikanth; Swetha, R.K.; Chakraborty, Somsubhra; Li, Bin; Weindorf, David C.; Dutta, Sudarshan; Banerjee, Hirak; Das, Krishnendu; Majumdar, Kaushik
• Is Part Of: Biosystems engineering. Volume 209(2021)
• Page Start: 154
• Page End: 169
• Publication Date: 2021-09
• Subjects: Soil organic matter -- Smartphone -- Random forest -- Image reflectance -- Illumination -- Perturbation validation; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2021.06.018
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : This study evaluated a novel smartphone-based soil image segmentation technique and subsequent machine learning (ML) optimization methodology with a set of soil images for rapidly predicting soil organic matter (SOM) with minimal soil processing. A smartphone and a custom-made box were used to capture images for 90 soil samples, collected from three different agroclimatic zones of West Bengal, India under three different illumination conditions. To offset the impact of variable illumination, the reflectance component of the image was recovered by removing the illumination from the image. Further, to deceive the ML model without distorting the soil image, an adversarial image was generated by adding Gaussian noise to the image. A Tree-based Pipeline Optimisation Tool was used to find an optimum ML stacking scheme using six different ML models. Model validation statistics indicated that reflectance image-extracted sub-colour space could predict SOM with reasonable accuracy (R 2 = 0.88, RMSE = 0.28%) using original images in stack one. Moreover, the sub-colour space using perturbed images in stack one could sense noise, worsening the model validation (R 2 = 0.79, RMSE = 0.36%). Conversely, seven out of eight tested colour spaces in stack two were unable to sense the image noise, producing higher validation performance than the original images. The proposed smartphone-based image acquisition setup combined with the computer vision and ML pipeline produced an important advance in affordable optical tool-based SOM prediction with significant time and cost savings. More research is warranted to extend this approach by incorporating field images of variable soil types taken under variable illuminations. Highlights: Smartphone captured soil images were used to predict SOM. The image reflectance was recovered to remove variable illumination. Adversarial images were generated by adding Gaussian noise to the original images. SOM prediction model produced good validation accuracy using original images. Inclusion of perturbed images was able to detect noise.