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As computer vision advances, the Philippines falls behind on the resources of a web-based tool that features image annotation. In line with this, this research develops a web application that users can use to crowdsource annotations from an uploaded file and have a web interface that summarizes all the stored images. Image annotation uses a computer and labels multiple methods in recognizing the data, classification, and object detection to identify and mark the prominent feature of an image to train the computer to recognize and identify an unlabeled image. It provides open access to any user and researcher who uses this app and collaborates with others. Although this app requires multiple image data to improve the app's overall performance, this app also features inputs from the user to widen the identifier feature of the app. This research used agile software development to ensure continuous development. In addition, the testing is used to continuously test and advise providers' feedback per step that the developers are doing. Ten evaluators are invited to test the system. The results show that the scenario completion time of using the CROMA Web Application is easy to use and navigate and uses the users' time well. The scores of each usability component show that the system is easy to navigate and that learns to use the system well. It is a good result and suggests that users can perform the specific task they need to do in the system. Furthermore, the user acceptance test scores of each usability component show that the web application is doing its function efficiently, and the users are satisfied and happy with the features. After three layers of agile testing, CROMA Web Application has been given the go-ahead to be deployed in the cloud server. For future work, automated labeling and a search function would help the user navigate preferences for every category of the images.
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