This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components: 1) Weather Station – This component includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration, among others, to collect real-time weather data. 2) Controller Unit – This unit is equipped with machine learning algorithms or models to estimate the reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data. This enables the system to determine the optimal irrigation amount based on plant needs automatically. 3) User Interface (UI) and Display – This section allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages. It also provides a display for monitoring and interaction with the system. 4) Irrigation Unit – This component is responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.
การเปลี่ยนแปลงสภาพอากาศของโลกทวีความรุนแรงขึ้นอย่างต่อเนื่อง สถานการณ์ดังกล่าวส่งผลกระทบ โดยตรงต่อภาคการเกษตร โดยเฉพาะในประเทศไทยที่มีแนวโน้มเผชิญกับปัญหาการขาดแคลนน้ำและความ ผันผวนของปริมาณน้ำฝน ซึ่งส่งผลต่อทั้งปริมาณและคุณภาพของผลผลิตทางการเกษตรโดยตรง ทั้งนี้ การบริหารจัดการน้ำในภาคเกษตรกรรมของประเทศไทยยังคงเผชิญกับข้อจำกัดหลายประการ เกษตรกรส่วนใหญ่ยังคงพึ่งพาประสบการณ์ส่วนตัวในการให้น้ำพืช ซึ่งอาจนำไปสู่การใช้น้ำที่ไม่มีประสิทธิภาพ เช่น การให้น้ำมากเกินความจำเป็นหรือน้อยเกินไปจนส่งผลกระทบต่อผลผลิต หรืออาจนำไปสู่ปัญหา เช่น การแตกใบอ่อน การร่วงของดอก และมีผลผลิตที่ไม่ได้คุณภาพ (Togneri et al., 2023) ในขณะที่ข้อมูลทาง วิชาการที่สามารถช่วยให้การบริหารจัดการน้ำมีความแม่นยำขึ้น เช่น ค่าอัตราการใช้น้ำของพืชอ้างอิง (Evapotranspiration: ETo) และค่าสัมประสิทธิ์พืช (Kc) กลับเข้าถึงได้ยาก เนื่องจากมีความซับซ้อนในการ คำนวณ อีกทั้งข้อมูลที่มีอยู่มักเป็นข้อมูลเฉลี่ยรายจังหวัดซึ่งไม่สามารถนำไปใช้ได้อย่างมีประสิทธิภาพในระดับ ฟาร์ม โครงการนี้จึงมุ่งเน้นไปที่การพัฒนาระบบปัญญาประดิษฐ์สำหรับการติดตามและควบคุมการให้น้ำพืชอัจฉริยะ โดยอิงข้อมูลสภาพอากาศซึ่งจะช่วยแก้ไขข้อจำกัดของเกษตรกรไทยในการเข้าถึงข้อมูลที่ ถูกต้องและการบริหารจัดการน้ำที่แม่นยำ
คณะวิศวกรรมศาสตร์
ยานยนต์ไฟฟ้าดัดแปลง
คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang has a vision for sustainable excellence. The mission is to develop learners to be ready for the digital world, develop educational innovations using research as a base, strategic management with good governance, and academic services that benefit society. In this activity, the group of students joined with the Embassy of the Russian Federation in the Kingdom of Thailand, the working group of the Thai Silk and Culture Promotion Association, and the working group of the National Research Office (NRCT) to integrate knowledge to design a silk outfit that combines Thai and Russian cultures, create a network of cooperation in arts, culture, technology, innovation, and dissemination of knowledge and the beauty of Thai silk. The objective is to develop the potential of teachers and students in creative design, listen to the work guidelines from the working group of the Thai Silk and Culture Promotion Association, the working group of the National Research Office (NRCT) via an online meeting. The team of teachers and students from King Mongkut's Institute of Technology Ladkrabang under the name of the "Love Silk" group designed a Thai silk outfit that combines cultures with the identity of Thai silk and studying traditions and cultures of the Russian Federation's clothing. They studied related literature and research documents and integrated knowledge into the design process, inspired by the concept of the Rajapataen outfit. Since the reign of King Chulalongkorn (Rama V) in 1872, together with the clothing culture of the Russian Federation, emphasizing Thai silk, this concept has gone through the process of creating and selecting the design concept (Concept Generation and Selection). The concept received from the embassy was first submitted for feedback on July 25, 2024. There was a suggestion to add more uniqueness to Thai silk through a fashion show presentation by the wife and grandson of the Ambassador of the Russian Federation. Therefore, the designed outfits are 1 set of women's clothes and 1 set of boys' clothes. The women's set has an inner shirt adapted from the royal outfit using silk fabric, Kon Ka-ed pattern, with two separate pieces: 1 shirt and 1 skirt. The jacket is a modified long suit style, plain silk, dark pink and red. The boys' set has a long-sleeved shirt adapted from the contemporary royal style, tailored with cream silk, long slacks tailored with raw betel silk, and a collarless coat with blue silk and lotus pattern. Adapted from the Rajapattan suit with a long collar in an international style. On August 2, 2024, the designed suit and the prototype of the raw fabric suit were brought to the ambassador's wife and nephew to try on. On August 30, 2024, the ambassador was met for the 4th time to bring the silk suit that was cut into a real silk suit. The shirt was given to the ambassador's wife and nephew to try on. It was worn to join the fashion show in the 13th "Thai Silk to the World" Silk Festival at the Naval Auditorium, where lecturers and students from the Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, joined the fashion show in the finale round, which proceeded smoothly. After the event, the working group of the Thai Silk and Culture Promotion Association brought the clothes designed and tailored by the "Rak Prae Mai" team to exhibit Thai Silk to the World Exhibition from September 1-8, 2024 at the Emsphere Shopping Mall. The team summarized the report and compiled a complete report. In the implementation of this project, the Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang has received support and facilitation throughout the project. The budget support from the National Research Council of Thailand (NRCT), the support of fabrics for sewing from the Thai Silk and Culture Promotion Association, and the information for designing valuable silk dresses from the Embassy of the Russian Federation in the Kingdom of Thailand are very important factors that made this operation a success. It is a very important experience for the team of teachers and students of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang. We sincerely hope to receive good cooperation in the future.
คณะวิทยาศาสตร์
Otitis Media is an infection of the middle ear that can occur in individuals of all ages. Diagnosis typically involves analyzing images taken with an otoscope by specialized physicians, which relies heavily on medical experience to expedite the process. This research introduces computer vision technology to assist in the preliminary diagnosis, aiding expert decision-making. By utilizing deep learning techniques and convolutional neural networks, specifically the YOLOv8 and Inception v3 architectures, the study aims to classify the disease and its five characteristics used by physicians: color, transparency, fluid, retraction, and perforation. Additionally, image segmentation and classification methods were employed to analyze and predict the types of Otitis Media, which are categorized into four types: Otitis Media with Effusion, Acute Otitis Media with Effusion, Perforation, and Normal. Experimental results indicate that the classification model performs moderately well in directly classifying Otitis Media, with an accuracy of 65.7%, a recall of 65.7%, and a precision of 67.6%. Moreover, the model provides the best results for classifying the perforation characteristic, with an accuracy of 91.8%, a recall of 91.8%, and a precision of 92.1%. In contrast, the classification model that incorporates image segmentation techniques achieved the best overall performance, with an mAP50-95 of 79.63%, a recall of 100%, and a precision of 99.8%. However, this model has not yet been tested for classifying the different types of Otitis Media.