International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 18, No. 5, 8 Oct. 2026
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Prediction of Dye Adsorption in Textile Industry using Machine Learning
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Author(s)
Archana Chaudhari
1,*
Satchidanand R. Satpute
2
Vivek Randad
1
Pratik Karde
1
Dipak Limbhore
1
Rushikesh Lonikar
1
Index Terms
Gray Level Co-occurrence matrix, GLCM, Machine Learning, Dye Absorption, PPM Prediction, Image Processing, Automated Quality Control, Predictive Modeling.
Abstract
Adsorption of dyes present in textile industry waste water is an important process for environmental remediation and pollution control. Dyes are complicated organic compounds used in textile dyeing processes. The presence of dyes in wastewater is of major environmental concern because of their recalcitrance and potential toxicity. Adsorption technology is a promising solution for the removal of dyes from textile wastewater. It uses adsorbent materials to capture and immobilize dye molecules from aqueous solution. Machine learning techniques have been used to reduce the analytical efforts for prediction of concentration of dye solution in parts per million using a UV spectrophotometer.
In the research work, a prediction technique using Machine Learning and image processing is used to identify the color of images of samples and corresponding adsorption of the dye. The suggested model has achieved correct dye concentration estimates using retrieved image attributes with 93% accuracy with experimental data.
Cite This Paper
Archana Chaudhari, Satchidanand R. Satpute, Vivek Randad, Pratik Karde, Dipak Limbhore, Rushikesh Lonikar, "Prediction of Dye Adsorption in Textile Industry using Machine Learning", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.18, No.5, pp. 223-233, 2026. DOI:10.5815/ijigsp.2026.05.11
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