Development of machine learning enhanced low-cost spectrophotometer for pesticide prediction

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dc.contributor.authorMurathathunyaluk, S.
dc.contributor.authorJinorose, M.
dc.contributor.authorJanpetch, K.
dc.contributor.authorChanthapanya, N.
dc.contributor.authorSombatsri, W.
dc.contributor.authorWongsricha, A.
dc.contributor.authorChawuthai, R.
dc.contributor.authorMansouri, S. S.
dc.contributor.authorAnantpinijwatna, A.
dc.date.accessioned2026-08-06T10:51:13Z
dc.date.available2026-08-06T10:51:13Z
dc.date.issued2025-05-15
dc.description.abstractConventional analytical methods for measuring pesticide concentrations, such as chromatography, offer high accuracy but require expensive instrumentation, prompting the investigation of cost-effective alternatives like smartphone-based spectrophotometers. Despite their potential, these methods face challenges related to assembly and precision, often requiring human intervention to select appropriate images for analysis. This study presents a novel, affordable spectrophotometer designed for integration with machine learning algorithms. The device captures images of two spectral bands and employs a six-step image processing methodology to prepare images for analysis. A machine learning model trained on four algorithms with feature selection and cross-validation demonstrates high accuracy in predicting chemical concentrations of coloured solutions. The approach achieves 98.5 % accuracy for KMnO<inf>4</inf> and 96.7 % for Carbosulfan solutions, comparable to high-end spectrophotometry devices. The design eliminates the need for human intervention, reducing biased selection and result manipulation. However, concentration estimation of non-coloured compounds remains inaccurate, indicating areas for further refinement.
dc.identifier.citationMeasurement Journal of the International Measurement Confederation, 248, 2025
dc.identifier.doi10.1016/j.measurement.2025.116890
dc.identifier.issn02632241
dc.identifier.other2-s2.0-85217679768
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16997
dc.sourceMeasurement Journal of the International Measurement Confederation
dc.subjectCarbosulfan
dc.subjectCross-Validation
dc.subjectImage Processing
dc.subjectMachine Learning
dc.subjectSpectrophotometer
dc.titleDevelopment of machine learning enhanced low-cost spectrophotometer for pesticide prediction
dc.typeArticle

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