Efficient durian orchard development requires integrating knowledge, technology, and innovation from farmers and academics to cope with environmental changes and market demands. The Durian Web-based Learning Hub is an online learning platform developed to serve as a central hub for knowledge transfer from experts and as a space for experience exchange among farmers. Users can access learning resources conveniently and continuously. This platform is part of the Innovation Project for Production and Marketing Information Management Innovation for Enhancing the Quality of Durian Production Entering into Premium Markets, supported by the Program Management Unit for Area-Based Development (PMUA)
การทำสวนทุเรียนในปัจจุบันเผชิญกับความท้าทายหลายด้าน ทั้งการเปลี่ยนแปลงสภาพภูมิอากาศ โรคและแมลงศัตรูพืชชนิดใหม่ และความต้องการของตลาดที่เปลี่ยนแปลงอย่างรวดเร็ว ในขณะที่องค์ความรู้และประสบการณ์ที่มีค่าของเกษตรกรและผู้เชี่ยวชาญกระจายอยู่ในหลายพื้นที่ การเข้าถึงข้อมูลและการแลกเปลี่ยนประสบการณ์แบบดั้งเดิมมีข้อจำกัดด้านเวลาและระยะทาง จึงจำเป็นต้องพัฒนาแพลตฟอร์มการเรียนรู้ออนไลน์ที่จะเชื่อมโยงผู้คนในวงการทุเรียนเข้าด้วยกัน เพื่อสร้างชุมชนแห่งการเรียนรู้ที่เข้มแข็งและยั่งยืน
คณะศิลปศาสตร์
As a value-added product of locally sourced fruits in Phang Nga, stevia-sweetened jelly offers a healthy and sustainable option for consumers seeking reduced sugar intake. This product has the potential to become a popular souvenir, promoting local agriculture and boosting the regional economy.
คณะวิทยาศาสตร์
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.
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