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Undergraduate Theses

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14578/5

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    Automated misting and android-based monitoring system for oyster mushroom (Pleurotus SP.) production
    Cabrera, Cedie Vince E. (Davao del Sur State College, 2024-06)
    This study was conducted to maintain optimal temperature and humidity, which is crucial for oyster mushroom growth; otherwise, it could impede mushroom development. An automated misting and Android-based monitoring system was developed to address this challenge. Other studies have used an automated mister; however, system monitoring is unavailable. Davao del Sur State College mushroom facility relies entirely on environmental temperature and humidity. The findings are as follows: A DHT11 sensor was used to read the temperature and humidity within a 5cm range from the sensor, spray mist when the DHT11 sensor detects a temperature of 28 degrees celsius and 72% relative humidity, HC04 Ultra-Sonic Sensor is used for water refill automation when the water reaches 7cm away from it, display humidity, temperature, and water level at 5 second intervals, and generate a graph based on average temperature, humidity, and water level calculated per month. The result of the study contributes to the agricultural aspects, specifically in mushroom farming, hence decreasing the farmer's workload and increasing the harvest yield, farming automation, and academic research. The research outcome is a foundation for new studies leading to a better understanding of farming with a more enhanced system for a better farming yield.
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    Android-based postharvest grading classification of cavendish banana using deep learning and internet of thing
    Caminade, Lilynn S. (Davao del Sur State College, 2024-06)
    This study is combined deep learning and the Internet of things, which used of microcontroller and android devices. An application and weight machine, may be risks of mistakes and consistent of grade classification result of image processing and difficulties in obtaining the weight value of the Cavendish banana. The Rapid Application Development is the help to established an application with weight measurements to capture images and classify the grade classification of the banana. The pretrained model using the teachable machine. The datasets gathered and captured the banana based on the postharvest grading classification: Class A, Class B, Cluster and Totally Reject. The android app is capable of captured image, classify the grading classification of the Cavendish banana with weight detected with the help of weight scale machine do perform and also the captured images and classify of the postharvest grading classification of the banana with weight measurement of the banana and displayed the information on the application. This study contributes to the modern-farming technology in the industries of agriculture and manufacturing to helps workers fasten the performance and productions and along with adapting new technology into agri-technology and also to the economy be more imports and exports globally and this study helps the agri-farming and plantation industries and in importing and exporting quality products globally.