Climate change affects agricultural systems worldwide, including Thailand, and may lead to reduced crop yields, impacting food security. Bambara groundnut is a crop with the potential to adapt to changing environments and can thrive in areas with limited resources. This research aims to study the impact of climate change on Bambara groundnut yields in Thailand using the DSSAT (Decision Support System for Agrotechnology Transfer) model, an important tool for predicting plant growth under various environmental conditions. This study utilizes climate data, soil composition, and genetic information of Bambara groundnut to simulate and analyze yield trends under future climate scenarios. Four study areas in Thailand were selected: Songkhla, Lampang, Yasothon, and Saraburi. The CSM-CROPGRO-Bambara groundnut model was used to assess the impact of changing temperature and rainfall on the growth and yield of Bambara groundnut. The results of this study are expected to provide farmers and researchers with valuable information for planning cultivation and managing peanut production in response to climate change. Additionally, the findings can help formulate policy guidelines to promote the cultivation of climate-resilient crops and support the country's food security.
การเปลี่ยนแปลงสภาพภูมิอากาศส่งผลกระทบต่อระบบเกษตรกรรมทั่วโลก รวมถึงการเพาะปลูกพืชอาหาร ถั่วหรั่ง (Bambara groundnut) เป็นพืชตระกูลถั่วที่มีความสามารถทนแล้ง เจริญเติบโตได้ดีในดินที่มีความอุดมสมบูรณ์ต่ำ และให้คุณค่าทางโภชนาการสูง จึงมีศักยภาพในการเสริมความมั่นคงทางอาหารของประเทศ อย่างไรก็ตาม การเปลี่ยนแปลงของอุณหภูมิ ปริมาณน้ำฝน และความแปรปรวนของสภาพอากาศ อาจส่งผลต่อการเจริญเติบโตและผลผลิตของถั่วหรั่ง ทำให้เกิดความไม่แน่นอนในระบบการเกษตร งานวิจัยนี้มีเป้าหมายเพื่อประเมินผลกระทบของการเปลี่ยนแปลงสภาพภูมิอากาศต่อผลผลิตถั่วหรั่งในประเทศไทย โดยใช้แบบจำลอง DSSAT (Decision Support System for Agrotechnology Transfer) ซึ่งเป็นเครื่องมือที่ช่วยจำลองสภาพแวดล้อมและพฤติกรรมการเจริญเติบโตของพืชภายใต้สภาวะต่างๆ การศึกษานี้จะช่วยให้สามารถคาดการณ์แนวโน้มผลผลิตของถั่วหรั่งในอนาคต และนำไปใช้ในการวางแผนการเพาะปลูก รวมถึงพัฒนากลยุทธ์ในการปรับตัวของเกษตรกรให้สามารถรับมือกับสภาพภูมิอากาศที่เปลี่ยนแปลงได้อย่างมีประสิทธิภาพ
คณะวิศวกรรมศาสตร์
The evaluation of mango yield and consumer behavior reflects an increasing awareness of product origins, with a growing demand for traceability to understand how the produce has been cultivated and managed. This study explores the relationship between mango characteristics and cultivation practices before harvest, using location identification to provide insights into these processes. To achieve this, a model was developed to detect and locate mangoes using 2D images via a Deep Learning approach. The study also investigates techniques to determine the real-world coordinates of mangoes from 2D images. The YOLOv8 model was employed for object detection, integrated with camera calibration and triangulation techniques to estimate the 3D positions of detected mangoes. Experiments involved 125 trials with randomized mango positions and camera placements at varying yaw and pitch angles. Parameters extracted from sequential images were compared to derive the actual 3D positions of the mangoes. The YOLOv8 model demonstrated high performance with prediction metrics of Precision (0.928), Recall (0.901), mAP50 (0.965), mAP50-95 (0.785), and F1-Score (0.914). These results indicate sufficient accuracy for predicting mango positions, with an average positional error of approximately 38 centimeters.
คณะอุตสาหกรรมอาหาร
Most rice is consumed as cooked, milled rice, but a small portion is also ground into flour or separated into a starch fraction and used by the food industry as a gluten-free ingredient. This study aims to find out if different types of rice flour and starch, such as white and colored rice, could be used in industry. This study employs green modification techniques to slow down the digestion process by combining polyphenols with starch. Our initial study found that the raw colored rice has a lower glycemic index than other types of rice, such as brown or white rice. Another study that looked at how the quality of colored rice flour was changed by different methods also discovered that out of the six green methods (annealing, heat moisture treatment, ultrasound, pregelatinization, wet-microwave, and dry-microwave). It found that ultrasound improved the polyphenol bioaccessibility in the rice flour and reduced the digestion rate. The pregelatinization process led to the flour having high solubility and an estimated glycemic index. Different techniques affected the flour/starch quality in different ways. Therefore, for further industrial application, it could also be easier to select the method for food product based on their required techno-functional quality of flour/starch. In addition to the modification techniques, this study showed that the high bioaccessible polyphenol content and high polyphenol content in rice greatly slowed down the rate of digestion. This study also open for further exploring the possibility of using high polyphenol agricultural waste to modify starch and flour in a sustainable manner.
คณะวิศวกรรมศาสตร์
This Project has been undertaken to address the need for skill development and knowledge enhancement in pneumatic systems and automation control, which are crucial in today’s manufacturing industry. Pneumatic systems play a vital role in various production processes, including machine control, automated devices, and assembly lines. However, the Department of Measurement and Control Engineering currently lacks a laboratory dedicated to the study and experimentation of pneumatic systems due to the deterioration and lack of maintenance of the previously used equipment. This has resulted in students missing the opportunity to practice essential skills required in the industrial sector. The authors of this thesis recognize the necessity of reviving and developing a pneumatic laboratory that can effectively support teaching, learning, and research activities. This project focuses on studying and developing industrial robotic arm control systems and pneumatic systems, integrating modern technologies such as Programmable Logic Controllers (PLC) and AI Vision. These systems are intended to be applicable to real-world industrial contexts. The outcomes of this project are expected to not only enhance the understanding of relevant technologies but also aim to transform the laboratory into a vital learning hub for current and future students. Furthermore, this initiative seeks to improve the competitiveness of students in the job market and support the development of innovations in the manufacturing industry in the years to come.