School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia.
Centre for eResearch and Digital Innovation, Federation University, Mount Helen, VIC 3350, Australia.
Sensors (Basel). 2023 Mar 16;23(6):3181. doi: 10.3390/s23063181.
Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only 9% for the top 5 predictions, the accuracy of the proposed method is 74%, which is significant.
土壤颜色是农业中监测土壤健康和确定其特性的最重要因素之一。为此,考古学家、科学家和农民广泛使用孟塞尔土壤比色卡。从图表中确定土壤颜色的过程是主观的且容易出错。在这项研究中,我们使用流行的智能手机从孟塞尔土壤颜色手册(MSCB)中的图像中捕捉土壤颜色,以数字方式确定颜色。然后将这些捕获的土壤颜色与使用常用传感器(Nix Pro-2)确定的真实颜色进行比较。我们观察到智能手机和 Nix Pro 提供的读数之间存在颜色读数差异。为了解决这个问题,我们研究了不同的颜色模型,最后通过探索不同的距离函数,引入了 Nix Pro 和智能手机捕获的图像之间的颜色-强度关系。因此,本研究的目的是通过调整智能手机捕获的图像的像素强度,从 MSCB 中准确确定孟塞尔土壤颜色。在没有任何调整的情况下,个别孟塞尔土壤颜色的确定准确率仅为前 5 个预测的 9%,而所提出方法的准确率为 74%,这是显著的。