The current system of checking and grading egg quality in the Philippines was done manually one by one using the traditional way where graders exert great effort that resulted in graders' visual stress. To address the problem identified the researchers proposed a scientific way of checking and grading the egg quality by using image processing based non-destructive and cost-effective technique to detect various cracks, dirt, and defect in eggs. Upon testing, the system obtained a total of 91.33% as high-quality eggs and the presence of either crack or dirt while 8.66% were inspected as low quality. For the internal part of each egg, the system achieved 100% detection of the yolk. The main results achieved have been quite promising; the researchers are encouraged to continue the labor of improving the generation of internal and external egg detection.
Fire incident is one of the most common undesirable events that cause damages toward houses and human. It can start anytime and anywhere without any prior hint when it would happen. With the cause it brings, the implementation of a device capable of detecting and providing notification is necessary. This study presents the method of early-stage fire-flame detection utilizing image processing integrated with the Android application for emergency notification. The proposed system utilized the image processing applying Convolutional Neural Network (CNN) integrated with mobile app as early notification to the end user. After executing 3999 iterations of 210 images are used for testing to get the Train Accuracy (how accurate the training is) and Cross entropy (how far is the prediction of the actual result), it resulted to 100% trained accuracy and 96% validation. The test evaluation accuracy result was 99% using a cached file of 210, it means that expect the model to perform ~99% accuracy on the new data. From the test that has been done, the proposed system was able to capture the fire flame images and send necessary notification real time. The test results show the captured actual images and the information sent to the end user. The summary test results verified that all gathered data was higher than 90% to be able to send notification. The android app used to show both the pre-investigation information useful for the house owner and BFP personnel.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.