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. Development of an In-line Sc-ise Sensor System for Closed Hydroponic Nutrient MonitoringNutrient monitoring is crucial in closed hydroponic systems, where accurate control over individual ion concentrations directly influences crop yield and fertilizer efficiency. While electrical conductivity (EC) sensors are commonly used, they only measure total ionic strength and cannot distinguish between specific ions. Liquid-contact ion-selective electrodes (LC-ISEs) have been studied as an alternative but tend to suffer from durability issues and signal instability under the high-flow conditions... Y. Jang, W. Cho |
3. The Development of a Real-time Monitoring System Using IoT Sensor TechnologyThis study developed an IoT-based monitoring system for cold storage of agricultural products. Using temperature-humidity, CO₂, and ethylene sensors with Raspberry Pi, real-time data were collected and analyzed. Field tests showed stable monitoring of temperature (3~8 °C), humidity (77~92%), and CO₂ (450~1400 ppm), while no ethylene was detected. The system demonstrated reliable performance and potential to improve quality control and efficiency in post-harvest storage. ... H. Joo kim |