Invasive Insight

What are invasive species

An invasive species is a non-native organism—be it a disease, parasite, plant, or animal—that, once introduced to a new environment, begins to proliferate and expand beyond its initial location. These species pose a significant risk of harming not just the local ecosystem, but also the economy and human health. While their spread is primarily facilitated by human activities—often unintentionally—their rapid dispersion is further accelerated by global trade and travel, which inadvertently transport these species to new regions. In the context of the United States, both deliberate and accidental introductions of invasive species can have devastating consequences.

Inspiration 🚀🚀

Tackling the issue of invasive species is more than just an ecological imperative; it's an opportunity to make a lasting impact on environmental conservation. Many individuals remain uninformed about the risks these non-native organisms pose to our ecosystems. By dedicating a project to this pressing challenge, we want to contribute to the protection of natural habitats and also play a crucial role in elevating public awareness. our project can serve as an educational platform, enlightening people about the significance of managing invasive species and mitigating their detrimental effects.

What it does

• Real-time density mapping of invasive species across the U.S., aiding environmentalists in risk assessment. • Trend analysis feature showing areas with increasing or decreasing invasive species populations. • Image-based classification system to identify whether an animal is invasive, powered by machine learning. • Gap in current offerings: No existing website provides image-based invasive species identification along with real-time density data. • Objective: To keep environmentalists and the public informed about the risks posed by invasive species in the U.S.

How we built it ⚙️⚙️

Initially, we engaged in data scraping, manually collecting 1,000 images for each category from sources such as the National Forestry, Google Images, and various other websites. Due to the limited availability of data, manual filtering was essential to maintain the model's accuracy. We had to standardize the collected data by resizing the images, as they came in varied dimensions. To process and analyze these images, we utilized TensorFlow for implementing convolutional neural networks and conducting image classification.

Challenges we ran into

Scarcity of Training Data: A main obstacle is the limited dataset available for training. Machine learning models used for classifying or predicting species need a large and diverse set of data for optimal performance. Inadequate data can lead to poor model results and inaccuracies.

Species Diversity Challenges: Invasive species can vary widely, appearing differently depending on their location and the surrounding environment. A dataset lacking this diversity can hamper the model's ability to generalize effectively.

Issues in Species Identification: Properly distinguishing species in images is difficult, particularly when the species look similar. A limited dataset increases the risk of incorrect classification, compromising the project's reliability.

Rare Species and Imbalanced Data: Some invasive species are seldom found, making it hard to gather adequate samples for training. This can result in unbalanced datasets, undermining the model's capability to accurately identify these rare invasive species.

Complexities in Density Data Mapping: Rendering density data on a geographical map is intricate and fraught with challenges.

Challenges in Map Design and Interactivity: Incorporating advanced features like tooltips or pop-ups for better data representation in the map posed its own set of difficulties.

Accomplishments that we're proud of 🎉🏆

We are proud of completing this app within 36 hours Through meticulous data filtering and preparation, we managed to maintain high accuracy rates for our machine learning model. Despite challenges, we effectively plotted real-time density data of invasive species on a U.S. map, providing valuable insights for environmentalists and policy makers. We had to stay up whole night in order for the proper functioning of the model and making a unique project. Through meticulous planning and division of tasks, we ensured that each team member could work simultaneously on different components of the project.