Journal of Engineering Technology Access (JETA) (Online)

Determination of Clay Content by Applying Machine Learning with Hydrometer Testing and Specific Gravity Analyses

Chosita Sukkanon, Jirawat Supakosol, Pattanasak Chaipanna — Thu, 03 Jul 2025 00:00:00 +0700

This study aims to analyze and compare hydrometer test results with fundamental soil properties while applying Machine Learning (ML), a branch of Artificial Intelligence (AI), to enhance the speed and accuracy of clay content prediction. The study utilized soil samples from Nakhon Phanom and Sakon Nakhon provinces, Thailand. The experimental process included specific gravity and hydrometer analysis. For ML model development, linear regression (LR) and random forest regressor (RFR) were compared to analyzing factors influencing clay content. The data evaluation was based on feature importance analysis and statistical correlation (Correlation Matrix). The application of 10-fold cross-validation ensured that the models did not suffer from overfitting and confirmed the stability of predictions when using hydrometer data from longer test durations. The results indicate that hydrometer readings at longer durations exhibit a strong correlation with clay content and significantly improve the prediction accuracy of LR and RFR. The highest R² values obtained were 0.93 for LR and 0.87 for RFR, demonstrating that longer hydrometer test durations lead to more accurate clay content predictions. ML method combined with the hydrometer readings at 180 minutes, the R² exceeds 0.75. Specifically, LR outperformed RFR at minute 240, suggesting that the linear model better explains data variance at this duration. This research concludes that incorporating ML with hydrometer test data significantly improves the accuracy of clay content predictions. The findings highlight the potential of ML applications in soil property analysis and geotechnical engineering design, leading to more efficient and reliable engineering solutions.

IoT-Based Intelligent Environmental Control for Minimizing Spring Onion Bulb Weight Loss: A Grey-Taguchi Optimization

Thu, 03 Jul 2025 00:00:00 +0700

Post-harvest deterioration of spring onion bulbs presents a significant challenge for smallholder farmers in regions like Nakhon Phanom, Thailand, where high ambient temperatures and fluctuating humidity accelerate crop quality loss. These environmental instabilities contribute to substantial economic losses due to reduced shelf life and market value. This study proposes an integrated solution by developing an Internet of Things (IoT) based intelligent environmental control system, optimized using the Grey-Taguchi L9 method, to minimize weight loss during storage. The experimental setup evaluated nine distinct environmental conditions comprising different combinations of temperature, relative humidity, and light intensity over a three-month storage period. The IoT system enabled real-time monitoring and automated adjustments of key environmental parameters through embedded sensors and actuators. Statistical analysis, including signal-to-noise (S/N) ratio calculations and Grey Relational Analysis (GRA), was employed to determine optimal storage conditions. The results demonstrated that temperature and relative humidity were the most influential factors affecting weight loss, with optimal settings identified as 20°C and 65% RH, respectively. Under these conditions, average weight loss was minimized to 5.2 grams, and the model achieved a high R-squared value of 99.74%. In contrast, light intensity was found to have a negligible effect. This research offers a practical and scalable post-harvest solution for resource-constrained agricultural communities. By combining low-cost IoT technology with multi-response optimization, the proposed system contributes to sustainable agriculture and enhances food security by reducing storage-related losses in perishable crops.