Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Prediction of Lettuce Spad Value During Growth by a Multi-Spectral Image Sensor Using Machine Learning ModelIn this study, we aimed to improve previous LR (Linear regression) model for prediction of lettuce SPAD value, and used several machine learning (ML) models such as SVR (Support vector regression), KNN (K-nearest neighbors regression), KRR (Kernel ridge regression), DTR (Decision tree regression), RFR (Random forest regression), and ANN (Artificial neural network). K-means clustering algorithm was used to separate lettuce sample from background, and the reflectance from multi-spectral images containing... H. Noh |
2. A Rapid Non-invasive Capacitive Platform for in Vitro Assessment of Insecticide- Induced Skin CorrosionThis study introduces a rapid, non-invasive, and highly sensitive method for evaluating skin corrosion. The platform combines a capacitive sensor with a screen-printed electrode coated in a skin-mimetic layer, allowing real-time monitoring of capacitance changes in surrogate skin before and after exposure to corrosive agents such as agricultural insecticides. The biomimetic coating, formulated from hexane, ethanol, and lanolin, reproduces the lipid composition of the human stratum corneum. Across... Y. Kung |
3. A Dilution-free Capacitive Sensing Platform for Rapid Detection of Honey AdulterationHoney adulteration has become increasingly prevalent, and consumers cannot easily verify authenticity without relying on specialized testing laboratories. Such approaches are time consuming and labor intensive, creating barriers to routine quality assurance. To streamline authenticity assessment, this study introduces a capacitive sensor as an alternative to conventional electrochemical impedance spectroscopy. The sensor directly interrogates undiluted honey and adulterated samples, eliminating... Y. Kung |