DEEP LEARNING ENABLED DEEPOR BEEL MIGRATORY BIRD DETECTION SYSTEM

This article proposes a Deep Learning enabled model to study migratory birds at Deepor Beel, an ecologically important wetland in Assam, North East, India. Migratory birds have a very important role in maintaining ecological balance and act as an indicator of ecosystem health. On the other hand, the traditional methods of bird monitoring which depends on manual observations are often very much time consuming and error prone. This article put forward an idea about how modern technologies like Deep Learning and Computer Vision can enhance wild life monitoring system. It discusses the use of techniques such as Convolutional Neural Network (CNN) and YOLO (You Only Look Once) model for automatic recognition and classification of different bird species from images and videos. The proposed system comprises of stages such as data collection, image annotation, model training and deployment.Besides that, the article focuses on the advantages of automated monitoring system including its improved accuracy with continuous observation and support for the goal of conservation. It also deals with challenges like weather condition and species similarity for the image quality along with the future scope such as incorporating acoustic sensors and GIS technologies. In total, the article highlights the importance of technology driven procedures for an effective and efficient conservation and management of wetland ecosystem at Deepor Beel.