Design a program on identifying Proliferation rate of HABs
Due to global population growth and industrialization, excessive inflow of causative nitrogen into rivers, and the increase in water temperature due to global warming, the occurrence of harmful algal blooms (HABs) is increasing. HABs can cause not only ecological destruction but also various social and economic problems. Additionally, consuming water from lakes with abundant toxic cyanobacteria can lead to liver damage, vomiting, abdominal pain, and even death if consumed over a long period. The first recorded occurrence of animal mortality due to HABs was in Australia in 1878, and since then, livestock and wildlife have suffered damages from HABs worldwide. Furthermore, the United States' Lake Erie has experienced frequent HAB occurrences since 2011, and in 2007, China faced social disruption when a massive HAB outbreak in Lake Tai, one of the freshwater lakes, resulted in a suspension of the water supply. In order to address these HAB occurrence issues and assess the severity of HAB events, several systems have already been established and potential solutions have been proposed. However, these systems have limitations such as being highly systematic and advanced in terms of equipment and configuration. They are often located only in periodically affected areas, and they involve substantial costs. Therefore, we aim to overcome these limitations and design a system that can effectively manage HABs.
Beyond Limits: An Intelligent Wheelchair for Inclusive Living
The aim of this project is to enhance the mobility of individuals with disabilities, particularly aiding them in navigating stairs and challenging terrains. Across the world, powered wheelchair employ various methods, primarily categorized into two: 1) tracked mechanisms and 2) robotic wheelchair utilizing intricate robotic systems. The design presented by our team belongs to the latter category, which is recognized for its lighter build when contrasted with the former. However, despite its lightweight structure, this wheelchair design incorporates equipment that renders it more cost-effective and practical than conventional designs within the same category. Our design integrates three distinct mechanisms to adjust the height and center of mass of the passenger during stair climbing and maintain balance and surface contact. Utilizing an array of sensors, it continuously monitors the position of the person on the wheelchair and the wheelchair on the surface. This data guides adjustments in the mechanisms, ensuring stability. This innovation harbors the potential for enhancing various functionalities, including: GPS integration for user navigation. Real-time monitoring of vital signs (e.g., heart rate, blood pressure, body temperature). In an emergency, this data can be transmitted to ambulance centers to pinpoint the individual's location and immediate assistance. A simplified ambulance request system, accessible via a single button press. Overall, this innovative wheelchair prototypes aims to revolutionize accessibility, granting enhanced mobility and independence to individuals with disabilities.
AGRO-GUARD:Machine Learning-Driven Plant Real-Time Disease Detection,Clustering and Community Notifications
Agro-guard aims to revolutionize disease identification and community-based projects in the field of agriculture. Integrating Machine learning, Computer vision, clustering, and community-based technology, this project helped farmers to detect their plant disease with their solution and for early warning of plant disease which was spreading in their community which helped in crop management. The research project is divided into three parts.First,Integrating Machine learning to detect and classify plant disease with their solutions.Second,Integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN),to identify disease and analyze the pattern within agricultural regions.Third,Establishing notification system to notify real-time alerts to farmers about disease spreading in particular region.The research is crucial because it solve one of the crucial problem of our community which is untimely detection of disease.The finding of the research highlight the effectiveness of Agro-Guard framework in early disease detection and community detection.The machine learning models achieved high accuracy in identifying common plant disease and clustering results the pattern in diseases that were very important for notifying the community.The significance of these findings is that it can build powerful system which will overall grow the production of crops and plants due to timely update of the disease prevailing in the community.It contributes in sustainability production of crops and plants which ultimately ensure the good livelihood of farmer.