Study on Parasites in Blackchin Tilapia and Value-Added Processing Parasites play a crucial role in affecting fish health and the balance of marine ecosystems. The study of parasites in fish is essential for assessing fish population status and their impact on the ecosystem. This research focuses on a preliminary survey of parasites in Blackchin Tilapia (Sarotherodon melanotheron) found in the waters of Chumphon Province to determine whether this species carries parasitic infections. The findings will provide valuable insights for managing marine resources and developing strategies for processing Blackchin Tilapia into food products to help control its population in the ecosystem. One of the value-added processing approaches for Blackchin Tilapia is the "Nai Hoi Hua Fu" product. This product involves deep-frying the fish to achieve a crispy and fluffy texture before mixing it with mango salad to enhance its flavor and make it more appealing. This processing method not only adds value to the fish but also serves as a practical solution for managing the Blackchin Tilapia population, which may impact the ecosystem. The study results indicate that no parasitic infections were found in either the internal or external organs of the sampled fish, suggesting that the marine environment in the study area is favorable for fish health. However, continuous research is recommended to monitor long-term ecological changes and evaluate the impact of Blackchin Tilapia on ecosystem balance to ensure sustainable resource management.
มีการแพร่ระบาดปลาหมอคางดำ ในพื้นที่จังหวัดชุมพร โดยเฉพาะปากแม่น้ำ ดังนั้น จึงมีความสนใจว่าปลาชนิดนี้แพร่กระจายลงสู่ทะเลหรือไม จึงสำรวจพร้มอศึกาาาการติดเชื้อปรสิต เนื่องจากอยากทราบว่าทำไมถึงแพร่กระจายในระบบนิเวศเร็วมาก และสามารถนำมารับประทานได้หรือไม่
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components: 1) Weather Station – This component includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration, among others, to collect real-time weather data. 2) Controller Unit – This unit is equipped with machine learning algorithms or models to estimate the reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data. This enables the system to determine the optimal irrigation amount based on plant needs automatically. 3) User Interface (UI) and Display – This section allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages. It also provides a display for monitoring and interaction with the system. 4) Irrigation Unit – This component is responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.
คณะอุตสาหกรรมอาหาร
The "PRIVARY" product is an innovative herbal jelly beverage designed to support weight management and promote health through the benefits of four Thai herbs: roselle, safflower, chrysanthemum, and bitter melon. These herbs are rich in active compounds such as flavonoids, beta-carotene, and anthocyanins, which help reduce blood lipids, prevent inflammation, and exhibit antioxidant properties. The product emphasizes convenience and caters to health-conscious consumers using advanced production techniques like Inverse and External Gelation to create spheres encapsulating key bioactive compounds. Additionally, the product aligns with sustainability goals by enhancing the value of Thai herbs and supporting local communities.