Efficiency enhancement of food recognition using artificial neural network

In this paper, we have a tendency to apply a artificial neural network (ANN) to the tasks of detective work and recognizing food pictures. Be- explanation for the wide diversity of styles of food, image recognition of food things is usually terribly difficulties. Be that as it may, deep learning has been indicated as of late to be a truly intense image recognition system, and ANN could be a dynamic way to deal with deep learning. We tend to connected ANN to the errands of food location and recognition through parameter change. We tend to made a dataset of the preeminent incessant food things in a publically available food-logging framework, and utilized it to recognition execution. ANN demonstrated fundamentally higher accuracy than did antiquated support-vector-machine-based routes with handmade choices. Furthermore, we tend to establish that the convolution bits demonstrate that shading commands the component extraction strategy. For food image discovery, ANN also indicated fundamentally higher accuracy than a customary procedure. Fundamentally higher accuracy than a customary strategy.