https://ejournals.swu.ac.th/index.php/sej Srinakharinwirot Science Journal Faculty of Science en-US Science Essence Journal 2985-0290 https://ejournals.swu.ac.th/index.php/sej/article/view/16530 <p>The red cotton tree flower (<em>Bombax ceiba</em> L.) is a local plant commonly found in Northern Thailand. It is used in Northern Thai cuisine and traditional Thai medicine. A previous study demonstrated the antioxidant properties of red cotton tree flowers in a formulated skincare product, specifically a sleeping mask gel. This study aimed to develop an anti-wrinkle facial skincare product containing red cotton tree flower extract. The flowers were extracted using 99% ethanol through maceration at room temperature. A 1.5% concentration of the extract was incorporated into the sleeping mask gel. An <em>in vivo</em> study was conducted to evaluate skin irritation and anti-wrinkle efficacy. Twenty volunteers aged 30–60 years (7 men and 13 women) participated in the study. The results showed no skin irritation among the volunteers, and the product effectively reduced wrinkles after one month of use. Additionally, it increased skin moisture within the first week of application. The volunteers expressed overall satisfaction with the product and reported no adverse reactions. This newly developed product not only highlights the effectiveness of red cotton tree flower extract but also demonstrates its commercial potential.</p> Ruthaphan Santianotai Kanokwan Kiattisan Thawatchai Lekdee Ramita Sattatipkul Sujaree Hirunsirivat Copyright (c) 2025 Science Essence Journal 2025-03-05 2025-03-05 41 1 1 11 https://ejournals.swu.ac.th/index.php/sej/article/view/16568 <p>This study was aimed at surveying wild fruit plant species in the Khok Phu Taka forest area and developing a system of plant leaf classification using AI technology (<em>Polylthia debilis Finet &amp; Gagnep</em> and other plant species). Data were collected from the Khok Phu Taka forest, and the AI system (development of AI by training images (N=145)) was developed to classify the leaves of <em>Polylthia debilis Finet &amp; Gagnep</em> and other plant species. The system accuracy was verified of 10% of the images by using YOLO v4. The data were analyzed by Confusion Matrix. The findings revealed that plant species in the study area include <em>Polylthia debilis Finet &amp; Gagnep</em>, <em>Polyalthia evecta (Pierre) Finet &amp; Gagnep</em>, Indian gooseberry (<em>Phyllanthus emblica</em>), and <em>Ziziphus oenoplia (L.) Mill. var. oenoplia</em>, etc. The data were then labeled to develop the system, and an accuracy of 93 % was achieved in classifying <em>Polylthia debilis Finet &amp; Gagnep</em> leaves and other species. In conclusion, the developed AI system increases the efficiency of plant species identification through leaf classification, while it provides the method to identify various plant species in the future.</p> Benjamas Dhinnabutra Jetsada Kumphong Parinyawatr Dhinnabutra Chayakom Phurimsak Copyright (c) 2025 Science Essence Journal 2025-03-05 2025-03-05 41 1 12 23