Detection And Alert System Of Invasive Flower Species Using Cnn

Abstract

The introduction of invasive species, often referred to as foreign species, to the native species occurs frequently through a variety of channels, including the air, birds, and insects. This might harm the environment in the area. Invasive plants can have a negative impact on natural ecosystems by reducing native biodiversity, altering species composition, removing habitat from native and dependent species, changing biogeochemical cycling, and changing disturbance regimes. There are a few ideas that have been made in earlier studies to prevent this, but in this study, we approach to solving this issue by combining artificial intelligence with an anomaly detection technique and image processing. We compile sample photos of each species of flower in the ecosystem and create a dataset of all local flower species. In order to create a dataset of all native flower species, we first collect sample pictures of each flower species in the environment. Analyse the image dataset quantitatively and programme a machine learning model to identify the species. In order for a qualified botanist to examine the plant and decide whether it is hazardous to the park’s ecology, it is important to identify any outlier or anomalous flower species that are found. Finding flowers in pictures is one of CNNs’ most well-known applications. For instance, a producer of sunglasses employed CNNs to recognise floral images in advertising photos. The training set in this instance included thousands of photographs of actual flowers. The photos were then appropriately recognised as flowers by the network. This is a great example of how effective CNNs can be when used properly. The user so they can look into the image’s origin.