PIXEL CLASSIFICATION USING U-NET

Abstract

The rapid advances in Deep Learning (DL) techniques have allowed rapid detection, localisation, and recognition of objects from images or videos. DL techniques are now being used in many different applications related to agriculture and farming and medical Science Images. In this work we are using Deep Learning techniques such as unet,pretrained unet and apply on CWIF data set for Anomaly Detection and anomaly is weed and on Electron Microscopy Dataset we are detecting mitochondria in hippocampus region of the brain we evaluate our model using different losses and evaluation metrics at the same time also telling the drawback and advantages of different models. If we can detect the images in the crops we can use different machines that can be used for real time detection and removal of weed from the field Our technology can distinguish between crop and weed plants in commercial fields where crop and weed grow near to one another and can tolerate plant overlap. Automated crop/weed discrimination allows for targeted weed treatment in weed management tactics to reduce expense and adverse environmental effects. The images of hippocampus region of the brain to detect mitochondria in the images and give lable to each pixel will it belong to mitochondria or not