Measurement of Ellman’s Essay Using a Smartphone Camera Coupled with an Image Processing Technique in CIE-LAB Color Space for the Detection of Two Pesticides in Water

In recent years, Thailand’s agriculture sector has seen a significant rise in pesticide usage due to its vital role in the economy and global food trade. However, the uncontrolled use of pesticides poses severe health and environmental risks. This research focuses on the detection of pesticide residues, particularly carbaryl and paraoxon-ethyl, which are prevalent due to their high efficacy in pest control but pose neurological health risks by inhibiting acetylcholinesterase (AChE) activity, potentially impacting human health. The developed method assesses pesticide concentration by measuring changes in the CIE-LAB color space using a smartphone camera. Testing several concentrations (0.2, 0.6, and 1.0 ppm) over time revealed significant differences via ANOVA (F = 32.8, p < 0.001) and a strong linear relationship with R² values from 0.9129 to 0.9973 through regression analysis. Additionally, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to further understand the relationship between color changes and pesticide type and concentration. PCA showed that the L* and b* values contributed most to explaining the variance in the data (96.24%), while PLS-DA provided classification models. These strong linear relationships between pesticide concentration and colorimetric changes showcase the method’s accuracy and potential for on-site pesticide monitoring in agricultural produce, emphasizing technological advancements in sustainable agriculture practices. This research presents encouraging findings from an inexpensive and straightforward method for detecting pesticide residues, suggesting that a specialized mobile application could enhance the implementation of the proposed system.