Garbage disposal is a very important task in a healthy and green environment. With spreading awareness among the citizens of India regarding the importance of a clean environment to decrease the consumption of natural resources and garbage disposal, the recycling industry is booming. The quantity of generated trash in day-to-day life is affecting land, water, and air which causes a serious threat to the aquatic species and their surroundings and ultimately to humans if not managed properly. Conventional garbage disposal systems are on the rise and need accurate and efficient segmentation and recognition mechanisms. This demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. To deal with this problem the garbage classification process is automated by building an image classifier using a convolutional neural network (CNN) and thereby decrease the time for the waste segregation and make it cost-effective. The idea behind making the process automated is to decrease human intervention and make this waste segregation process more productive. In this work, three different models are being tested for higher accuracy: Simple CNN, ResNet50, and VGG16 trained on various datasets of images, these are used to extract features from images and feed them into a classifier for dump/trash classification. The trained models are then fed to a mobile application that captures pictures in the real-time using camera. The experimental results conclude the performance of VGG16 using Transfer Learning being significantly higher than all other models for the purpose of Trash Classification and the performance of Simple CNN being better for Dump Classification. © 2021 IEEE.