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

GÖZÜKARA G., Hartemink A. E., Zhang Y.

COMPUTERS AND ELECTRONICS IN AGRICULTURE, cilt.204, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 204
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.compag.2022.107524
  • Dergi Adı: COMPUTERS AND ELECTRONICS IN AGRICULTURE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Communication Abstracts, Computer & Applied Sciences, Environment Index, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Soil color, Digital image, Image processing, Machine learning, MATTER CONTENT, COLOR SENSOR, CAMERA, MODELS, TEXTURE
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

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.