In raising crickets for meat consumption, the growth rate and growth period of crickets are important data used to identify the number of crickets per breeding area at each age. Therefore, the researcher has an idea to create a system for monitoring the growth rate of crickets in a closed system using an infrared camera combined with computer image processing to study the growth and identify the growth period of crickets at each age in order to obtain knowledge that can be disseminated to farmers to improve the breeding process for maximum efficiency.
ปัจจุบันจิ้งหรีดถือได้ว่าเป็นสัตว์เศรษฐกิจชนิดใหม่ของประเทศไทยซึ่งทางภาครัฐโดยเฉพาะกรมปศุสัตว์ได้เริ่มมีการส่งเสริมให้ภาคการเกษตรได้เพาะเลี้ยงจิ้งหรีดเพื่อการบริโภคสำหรับการส่งออก และเป็นการตอบรับกับเทรนด์อุตสาหกรรมอาหารใหม่ (Novel food) ตามแนวทางขององค์การอาหาร และเกษตรแห่งสหประชาชาติ (FAO : Food and Agriculture Organization) ซึ่งคาดการณ์เอาไว้ว่าจำนวนประชากรโลกจะเพิ่มขึ้นอย่างต่อเนื่องทำให้ความต้องการแหล่งโปรตีนมีมากขึ้นตามไปด้วย คณะผู้วิจัยจึงมีแนวความคิดที่จะหาสร้างระบบเลี้ยงจิ้งหรีดที่มีประสิทธิภาพ
คณะเทคโนโลยีการเกษตร
This research aims to evaluate the efficiency of nano-type oxygen diffusers at different pump power levels in sea bass nursery ponds. The study examines how varying power levels affect dissolved oxygen distribution in the water and their impact on the health, growth, and survival rates of sea bass. The findings indicate that pump power levels influence dissolved oxygen concentration, with the optimal power level improving oxygen distribution in the pond. This enhancement leads to higher survival and growth rates for sea bass. The results provide valuable insights for selecting appropriate oxygen diffusers and pump power levels in fish nursery pond systems. The experiment consisted of two conditions: 1. Without fish – This condition assessed the oxygenation capacity, oxygen transfer coefficient, oxygen transfer rate, and oxygen transfer efficiency of pumps at three different power levels. 2. With fish – This condition evaluated whether the oxygen supplied by pumps at three power levels was sufficient, based on the growth rate and survival rate of the fish in the pond. Blood counts were conducted to assess the immune response. The collected data were statistically analyzed using the RCBD method for the condition without fish and the CRD method for the condition with fish, employing SPSS software.
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.
คณะวิศวกรรมศาสตร์
This project has been developed to address medical challenges related to the process of counting and classifying blood cells from samples, a task that requires both time and high precision. To reduce the workload of medical personnel, the developers have created a platform and an artificial intelligence (AI) system capable of automatically classifying and counting cells from sample images. This system is designed to assist medical laboratory technicians by enabling them to work more efficiently and accurately, reducing the time required for analysis. Furthermore, it promotes the advancement of medical technology, ensuring effective usability from classrooms and laboratories to hospitals.