Illumination levels affect the prediction of soil organic carbon using smartphone-based digital images

被引:6
|
作者
Gozukara, Gafur [1 ,2 ]
Hartemink, Alfred E. [2 ]
Zhang, Yakun [2 ]
机构
[1] Eskisehir Osmangazi Univ, Dept Soil Sci & Plant Nutr, TR-26160 Eskisehir, Turkiye
[2] Univ Wisconsin Madison, Dept Soil Sci, FD Hole Soils Lab, 1525 Observ Dr, Madison, WI 53706 USA
关键词
Soil color; Digital image; Image processing; Machine learning; MATTER CONTENT; COLOR SENSOR; CAMERA; MODELS; TEXTURE;
D O I
10.1016/j.compag.2022.107524
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Soil organic carbon (SOC) analyses based on wet chemistry are labor-intensive and time-consuming and smartphone-based digital images have been used as a rapid, and low-cost alternative for the assessment of SOC content. We explored the effect of four illumination levels (300, 600, 900, and 1,200 lx) on the prediction ac-curacy of SOC using smartphone-based digital images. A total of 611 surfaces (0-20 cm) soil samples were collected from under pasture, agriculture, and in a former lakebed. Under controlled conditions, digital images were captured using an Apple iPhone 11 under four illumination levels. A total of 5 color coordinates (RGB, HSV, CIE L*a*b*, CIE L*u*v*, and CIE XYZ) were extracted from the images for each illumination level, and these were used to predict SOC by means of Lasso, elastic net (en), ridge, random forest (rf), and support vector machine (svm) models. The R, G, B, S, V, L*, a*, b*, u*, v*, X, Y, and Z color parameters were significantly affected (P < 0.05) by the illumination levels. The 300 lx illumination level had a fair prediction accuracy for SOC (r2 = 0.42 to 0.70) using random forest. The a*, R, and H color parameters were the best variables based on the 300 lx dataset for the prediction of SOC. We conclude that illumination levels significantly affected the prediction accuracy of SOC; lower illumination levels improved the prediction accuracy of SOC content.
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页数:8
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