This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.
The increasing complexity of pharmaceutical treatments requires precise pill identification to ensure patient safety. Traditional methods for pill reconciliation rely on human experts, which are time-consuming and prone to errors. Deep Convolutional Neural Networks (CNNs), particularly effective in image processing, offer a promising solution for automating and enhancing these processes.
คณะเทคโนโลยีการเกษตร
The design of a 50-rai public park in the Lat Krabang district of Bangkok aims to provide a recreational space for urban residents in Lat Krabang and nearby areas. The focus is on user groups such as students, university students, and working individuals, incorporating the concept of Universal Design to ensure that everyone in society can use the space equally. However, there is still an emphasis on creating active recreational areas to meet the sports and exercise needs of students, university students, and working individuals. The design of the Lat Krabang area, which is a low-lying region resembling a basin, includes features for water retention, water management, and water treatment for use within the park. The area will focus on exercise, sports, running, walking, relaxation, and educational garden spaces.
คณะเทคโนโลยีการเกษตร
This study investigated the effects of seed priming with Chaetomorpha sp. seaweed extract on seed germination and seedling growth of chili pepper. The objective was to examine the influence of seaweed extract concentrations on seed germination and seedling development. Seeds were primed in different concentrations of Chaetomorpha sp. extract, compared with a control treatment. The experiment was conducted using a completely randomized design with four replications. Results showed that seed priming with seaweed extract enhanced seed germination characteristics. Primed seeds exhibited improved germination percentage, germination index, and germination rate compared to the control. Additionally, seedlings from primed seeds showed enhanced root and shoot development. This study demonstrates the potential of Chaetomorpha sp. extract as a promising seed priming agent for improving chili pepper seed quality, which can be applied in the production of high-quality chili pepper seedlings.
คณะเทคโนโลยีการเกษตร
This research investigates the traditional knowledge, biological characteristics, and bioactive compounds of Melaleuca cajuputi Powell, with a focus on its conservation and sustainable utilization. The study encompasses its applications in agriculture, healthcare, and bioenergy.