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DISTINGUISHING HEAVY METALS CONCENTRATION IN GREEN LEAFY VEGETABLES BY USING THE RGB COLOR MODEL.
- Source :
-
Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development . 2024, Vol. 24 Issue 1, p395-406. 12p. - Publication Year :
- 2024
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Abstract
- The objective of this research was to study of the correlation between RGB colour indicators and lead concentration in leafy plants. Cabbage and lettuce crops were watered with 3 levels of Lead Pb-contaminated (2.4 and 6 mg/lit). To distinguish the heavy metal contamination and their impact on vegetative characteristics for plants, the results showed with the levels of poisoning (0,2,4, and 6 mg/lit) showed the maximum value of Hue and vegetative were 0.76. and 0.032, also showed the minimum value for the same indices were 2.15 and 1.51. Also with the levels of poisoning (0,2,4, and 6mg/lit) showed the maximum value of simple red-green ratio and Green-red vegetation index was 1.61. and 0.23, also showed the minimum value for the same indices were 1.28 and 0.12. for Cabbage crops while for lettuce the results showed with the levels of poisoning (0, 2, 4, and 6 mg/lit) showed the maximum value of Hue and vegetative were 0.71. and 0.027, also showed the minimum value for the same indices were 0.41 and 0.024. Also with the levels of poisoning (0,2,4, and 6 mg/lit) showed the maximum value of simple red-green ratio and Green-red vegetation index was 1.65. and 0.43, also showed the minimum value for the same indices were 1.6 and 0.2. Linear regression analysis was performed on the equations to predict the monitoring Hue and vegetative and simple red-green ratio and Green-red vegetation index The red, green, blue band and intensity, the simple blue-green ratio addition to visible atmospherically resistant index simple green leaf and normalized greenblue difference index The RGB-based vegetation index 2 and RGB-based vegetation index 3 at different poisoning levels. The existence of a strong relationship between them and contains a high coefficient of determination. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 22847995
- Volume :
- 24
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development
- Publication Type :
- Academic Journal
- Accession number :
- 178079143