Dimas Firmanda Al Riza

The non-destructive estimation of the corn kernel moisture content is essential for determining the optimal harvest period. Although various spectrometer sensors are currently available, their predictive performance differs due to variations i...

Accurate species authentication of pork (Sus scrofa domesticus) is essential for food labeling integrity and religious dietary compliance. However, conventional PCR-based methods are hindered by bulky instrumentation and lengthy thermal cycling, limiting their point-of-need applicability. This study describes the development of a compact, low-cost (< 300 USD), real-time isothermal recombinase polymerase amplification (RPA) platform optimized for on-site porcine DNA detection. The device features a novel reaction chamber fabricated from polymethyl methacrylate (PMMA) via a rapid 5-minute milling process. To ensure efficient heat transfer, a high-thermal-conductivity grease (10 W/mK) is utilized at the heating interface, providing superior thermal contact and stability compared to traditional PDMS-based microfluidics. For signal transduction, an integrated AS7262 spectral sensor provides high-sensitivity digital fluorescence monitoring at 500 nm, utilizing a 488 nm ice-blue laser and a 90° orthogonal optical geometry to minimize background scatter. Operating at a constant 37 °C maintained by a PI control algorithm, the system—coupled with a rapid 10-minute thermal extraction protocol for pork meats—achieves a total turnaround time of 30 minutes. The platform demonstrates a limit of detection (LOD) of 1 pg/µL, with a linear correlation (R2 = 0.9014) between the Time-to-Threshold and the log-transformed DNA concentration. The total sample-to-result time for complex processed meat matrices is established at 45-50 minutes due to matrix-induced kinetic delays. Successful validation via gel electrophoresis confirmed a specific 152 bp amplification product. By bridging the gap between sophisticated laboratory analysis and field-based inspection, this integrated platform offers a robust, cost-effective solution for rapid meat speciation and global food supply chain monitoring.

Soil nutrients, such as nitrogen, phosphorus, and potassium, are critical for plant growth and agricultural productivity. Conventional laboratory methods for measuring these nutrients are accurate but often time-consuming, costly, and environmentally taxing. This study explores the potential of portable visible-near infrared (Vis-NIR) spectrometer combined with machine learning algorithms as a rapid, cost-effective, and eco-friendly alternative for soil nutrient analysis. Soil samples of clay, clay loam, and sandy clay were collected and analyzed using artificial neural network (ANN) approach to predict soil nutrients. A total of 81 reflectance spectra data from each soil type were acquired using an AS7265x sensor and processed to develop a predictive model for nutrient content. ANN models demonstrated high accuracy, with R² values exceeding 0.8 in each type of soil texture. This study emphasizes the potential of portable Vis-NIR spectrometer and machine learning integration to revolutionize soil nutrient analysis, offering significant improvements in agricultural efficiency and sustainability.

Grape cultivation has gained increasing attention due to its short growing period and the high market value of its sweet, refreshing fruits. However, achieving optimal growth requires precise environmental and nutrient management, which can be challenging under conventional farming practices. This research aims to develop an automatic watering system that integrates soil moisture and nutrient monitoring to optimize grape cultivation. The system utilizes Nitrogen Phosphorus Potassium (NPK) sensors, soil moisture sensors, and a camera for growth observation, all connected through the internet of things (IoT) for remote monitoring via Android devices. A fuzzy logic controller is implemented to regulate watering duration based on environmental conditions such as temperature and humidity. Experimental results show that the system effectively adjusts watering duration to approximately six seconds when the temperature is between 25–32 °C and humidity is around 60%. The DS18B20 temperature sensor achieved an average error rate of only 0.12%, while the humidity sensor demonstrated 0.2% error, indicating high accuracy levels of 99.8%. Despite minor limitations related to internet stability and sensor calibration, the system demonstrates strong potential for commercial-scale smart farming applications, promoting resource-efficient and data-driven grape cultivation.