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คณะเทคโนโลยีการเกษตร
The objective of this experiment was to determine the effect of nitrogen and potassium concentration combination with photoperiod on the growth of Viola in a plant factory to increase the quality of the products, reduce the production time and increase the production cycle throughout the year. The experimental plan was 3x3 Factorial in CRD with nine treatments and three replications (six plants per replication). The factor of this study was two factors; the first factor was three different concentrations of nitrogen and potassium in ratios of 1:1, 1:2 and 2:1. The second factor was the application of different photoperiods. There were 1) 24-hours photoperiod, 2) 8-hours light/16-hours dark photoperiod (Induced flowering state: 13-hours light/11-hours dark photoperiod) and 3) 5-hours light/3-hours dark photoperiod. Controlled temperature at 25 °C, the EC=1.5-2.0 mS/cm and the pH=5.8-6.5 in all treatment. The result showed that the concentration of N: K in the ratio of 1:1 combined with 24-hour photoperiod was the most vegetative growth and also maximizes reproductive growth. The overall great sensory evaluation was an acceptable level and suitable for cooking or decorating dishes. Therefore, the concentration of N: K in the ratio of 1:1 combined with 24-hour photoperiod is the best treatment to increase the quality of the product, reduce the production time of viola flowers in each cycle from 90-100 days down to 43-45 days which is good for farmers.
คณะอุตสาหกรรมอาหาร
This study aimed to develop a formula and production process for snacks made from germinated brown rice flour and banana flour using the extrusion process. The results indicated that both germinated brown rice flour and banana flour could be effectively used as the main raw materials for snack production via extrusion. The proportion of flour in the formula and production conditions, such as moisture content of the raw materials, barrel temperature, and screw speed, significantly influenced the nutritional value, bioactive compound levels, and antioxidant activity of the final products.
คณะเทคโนโลยีสารสนเทศ
This research presents the development of an AI-powered system designed to automate the identification and quantification of dental surgical instruments. By leveraging deep learning-based object detection, the system ensures the completeness of instrument sets post-procedure. The system's ability to process multiple images simultaneously streamlines the inventory process, reducing manual effort and potential errors. The extracted data on instrument quantity and type can be seamlessly integrated into a database for various downstream applications.