Semester 2 2023 Web Development
CodeCritters
Description
Pests, whether in the form of insects, rodents, or other unwanted organisms, acutely affect New Zealand's ecology, economy, and public health. The goal of our project was to develop an application that expedites the process of detecting pests. We have developed a web-based, local, and mobile application to classify insects and organisms accurately. Our application allows users to upload images of insects and organisms that are then classified by neural networks and returned to the user in the form of classifications and confidence levels, which are downloadable in the form of a CSV or Excel file.
Features
š¦ Upload images for prediction. š¦ Choose from various machine-learning models. š¦ Save or delete predictions in CSV or XSLX format. š¦ Make batch queries for simultaneous results. š¦ View results and confidence scores accompanied by comprehensive taxonomic and occurrence information. š¦ Register an account to access additional functionality. š¦ Login to access user history and logged-in status. š¦ Logged-in users have prediction results automatically saved that are accessible on the history page. Track and manage query data and interact with prediction results similarly to the detection page. Users can filter, sort, search, delete, and download previous queries. š¦ Platform accessible on desktop, web, or mobile. ā¢ Deployed Web Application. ā¢ Desktop application offering functionalities akin to the web application available for download from CodeCritters GitHub releases (excluding user accounts). ā¢ Android mobile app allows users to capture images for insect classification from the comfort of their mobile device. This is available for download from the CodeCritters FlutterApp Github release.
developed by CodeCritters
No comments yet, Start the conversation.