The project focuses on designing and creating Art Toy Mascots that reflect the identities of the 12 departments in the Faculty of Agricultural Technology. It combines the concepts of art and agricultural technology to promote better understanding and easy recognition of each department. The project utilizes creative design and artistic toy production techniques.
ในยุคปัจจุบันที่เทคโนโลยีมีบทบาทสำคัญในการพัฒนาภาคการเกษตร คณะเทคโนโลยีการเกษตรจึงมุ่งเน้นการปลูกฝังองค์ความรู้ควบคู่ไปกับการพัฒนาทักษะความคิดสร้างสรรค์ เพื่อให้นักศึกษาสามารถประยุกต์ใช้ความรู้ทางวิทยาศาสตร์และเทคโนโลยีในการแก้ปัญหาและพัฒนาภาคการเกษตรได้อย่างมีประสิทธิภาพ โครงการออกแบบและสร้างสรรค์ Art Toy Mascot จึงเกิดขึ้นจากแนวคิดที่ต้องการ ผสมผสานศิลปะและเทคโนโลยีเข้าด้วยกัน โดยใช้ Art Toy ซึ่งเป็นผลงานศิลปะที่กำลังได้รับความนิยมในกลุ่มคนรุ่นใหม่ เป็นสื่อกลางในการนำเสนออัตลักษณ์ของ 12 สาขาวิชาในคณะเทคโนโลยีการเกษตร
คณะเทคโนโลยีการเกษตร
This research aimed to (1) analyze the problems and needs in designing labels for catfish strip products of the Lam Sai Phatthana Community Enterprise Group, (2) develop the labels for catfish strip products, and (3) evaluate the satisfaction levels of consumers and community group members with the developed labels. The study involved 17 members of the community enterprise group and 151 consumers. Research methods included in-depth interviews and questionnaires to gather satisfaction data. The results showed that the newly developed product labels were effective in attracting attention and building trust in the product. The satisfaction levels among the Lam Sai Phatthana Community Enterprise Group members and consumers were high (x ̅= 4.17 and 3.75, respectively).
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.