Effect of colour calibration on the prediction of soil organic matter content based on original soil images obtained from smartphones under different lighting conditions

The ability to quickly and accurately determine soil organic matter (SOM) content is critical for effective soil management decisions. Using the colour of soil images captured by smartphones to predict SOM content has emerged as a promising alternative to traditional wet chemistry methods. However, natural environments can present a complex array of light conditions that can compromise the accuracy and consistency of soil image colour acquisition, thus limiting the method's applicability. To address this issue, we propose five colour calibration patterns (C0 (no calibration), C1 (neutral grey), C2 (RGB), C3 (RGBCMY), and C4 (24 colours)), based on a 24-colour standard card. These patterns were used to calibrate the images of 352 original soil samples obtained from smartphones in three different lighting environments - L1 (100-2000 lx), L2 (35,000-40,000 lx), and L3 (75,000-80,000 lx). Random forest models were used to construct predictive models of soil organic matter (SOM) content based on images. Our findings indicate that smartphones exhibit complex spectral response characteristics, which result in poor image accuracy and stability of uncalibrated (C0) images under varying lighting conditions. The uncalibrated (C0) soil images in different lighting environments exhibited high colour difference (Delta Emean = 14.11), resulting in poor SOM model sharing performance (R2mean = 0.52 and RMSEmean = 20.33 g/ kg). The use of colour calibration methods reduced the colour difference between soil images (Delta Emean = 8.19) and improved the shared accuracy of the model (R2mean = 0.61 and RMSEmean = 12.00 g/kg). The pattern of colour calibration has a key impact on the performance of the model application. The model sharing accuracy was found to be higher for the same or similar colour calibration pattern combination compared to different combinations of colour calibration patterns. Overall, the richer the colour calibration blocks, the better the model's shared performance. The findings of this research can enhance the application performance of soil attribute prediction models based on the colour of objects captured by smartphones in natural environments.