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 (∆E = 14.11), resulting in poor SOM model sharing performance (R = 0.52 and RMSE = 20.33 g/kg). The use of colour calibration methods reduced the colour difference between soil images (∆E = 8.19) and improved the shared accuracy of the model (R = 0.61 and RMSE = 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.

中文翻译：

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不同光照条件下智能手机获取的原始土壤图像颜色校准对土壤有机质含量预测的影响

快速准确测定土壤有机质 (SOM) 含量的能力对于有效的土壤管理决策至关重要。利用智能手机捕获的土壤图像的颜色来预测 SOM 含量已成为传统湿化学方法的一种有前途的替代方法。然而，自然环境可能会出现一系列复杂的光照条件，这可能会影响土壤图像颜色采集的准确性和一致性，从而限制了该方法的适用性。为了解决这个问题，我们基于24色标准卡提出了五种颜色校准模式（C0（无校准）、C1（中性灰色）、C2（RGB）、C3（RGBCMY）和C4（24色））） 。这些图案用于校准从智能手机在三种不同照明环境下获得的 352 个原始土壤样本的图像 - L1 (100–2000 lx)、L2 (35,000–40,000 lx) 和 L3 (75,000–80,000 lx)。使用随机森林模型构建基于图像的土壤有机质（SOM）含量的预测模型。我们的研究结果表明，智能手机表现出复杂的光谱响应特性，导致不同照明条件下未校准 (C0) 图像的图像精度和稳定性较差。不同光照环境下的未校准（C0）土壤图像表现出较高的色差（ΔE = 14.11），导致SOM模型共享性能较差（R = 0.52和RMSE = 20.33 g/kg）。颜色校准方法的使用减少了土壤图像之间的色差（ΔE = 8.19）并提高了模型的共享精度（R = 0.61 和 RMSE = 12.00 g/kg）。颜色校准的模式对模型应用程序的性能具有关键影响。发现与颜色校准图案的不同组合相比，相同或相似的颜色校准图案组合的模型共享精度更高。总体而言，颜色校准块越丰富，模型的共享性能越好。这项研究的结果可以增强基于智能手机在自然环境中捕获的物体颜色的土壤属性预测模型的应用性能。