Abstract: Banana French Fries This project aimed to study and develop the product Banana French Fries, which is a snack made by frying bananas in a form similar to French fries, in order to add value to bananas and create new choices for consumers. The experiment consisted of selecting suitable banana varieties, developing a coating formula, and testing the taste of samples. The results of the study found that Nam Wa bananas are the most suitable for making banana French fries because they have a firm texture and naturally sweet taste. The best coating formula consists of wheat flour, eggs, and milk, which provide longer crispiness. The taste test found that most consumers gave a very good response and were satisfied with the taste and texture. This project shows that banana French fries are a product with potential to be developed as a healthy snack and can be further developed into a commercial product in the future.
เปลี่ยนจากการบริโภคมันฝรั่งจากเดิมให้มีความแตกต่างจากปกติให้ลูกค้ากลุ่มใหม่ได้รับประทานผลิตภัณฑ์รูปแบบใหม่จากล้วยและได้ช่วยให้เกษตรกรได้มีรายได้ในส่วนนี้ด้วย
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.
คณะวิศวกรรมศาสตร์
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คณะเทคโนโลยีการเกษตร
Soil is home to a diverse array of living organisms that interact within a complex food web, facilitating energy and nutrient cycling essential for sustaining life above ground. Among these organisms, soil microbes play a crucial role in supporting plant growth. Beneficial microorganisms enhance nutrient availability, improve soil structure by increasing porosity, and strengthen plant resistance to diseases. Conversely, harmful microorganisms, such as plant pathogens, can hinder plant growth and reduce crop yields when present in high concentrations. Neutral microorganisms, which naturally inhabit the soil, contribute to the soil ecosystem without directly impacting plants. A single teaspoon of soil contains over a billion microorganisms, yet only about 1% of them can be cultured in laboratory conditions. This highlights soil as one of the richest reservoirs of microbial diversity on Earth.