Currently, Thailand is facing the issue of an aging agricultural workforce and a shortage of labor. The hiring process for agricultural workers is primarily based on word-of-mouth within a limited, narrow area. This may have indirect negative effects in various aspects, such as the lack of basic selection criteria (experience, skills), budget control, and hiring processes. As a result, the idea of creating a job platform specifically for farmers was conceived.Currently, Thailand is facing the issue of an aging agricultural workforce and a shortage of labor. The hiring process for agricultural workers is primarily based on word-of-mouth within a limited, narrow area. This may have indirect negative effects in various aspects, such as the lack of basic selection criteria (experience, skills), budget control, and hiring processes. As a result, the idea of creating a job platform specifically for farmers was conceived.
ต้องการให้เป็นช่องทางสำหรับเกษตรกรที่ว่างงานหลังจากหมดฤดูการเก็บเกี่ยว สามารถหางานเพื่อเพิ่มรายได้ให้กับตนเองและครอบครัวหรือเปิดโอกาสให้บุคคลนอกสาขาการเกษตรเข้ามาทำงาน โดยคนที่จะเข้ามาใช้ฟีเจอร์นี้ได้ต้องเป็นสมาชิกของธนาคารเพื่อการเกษตรและสหกรณ์การเกษตร เท่านั้น
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
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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.
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.