Image Processing for Crop Yield Classification

Crop yield classification and identification is an important task as it helps to differentiate between the qualities of the crop yield. The current manual method of examination is very cumbersome and moreover this method also takes a lot time and is rather not accurate. To overcome this challenge and for rather accurate results we will be implementing convolutional neural network for the identification of the crop types. Now convolutional neural network reduces the error to about 0.23 when applied on the identification of crops types from the given data set. Convolutional neural network demonstrates the ability to spot crop from a wide range of angles, including upside down, even when partially occluded with competitive performance. So as to reduce the error even further we will be using advanced Image processing techniques to produce multiple images from a single image. This technique is known as Image dataset generation and is highly popular in data science community because of the large number of dataset required in training of the convolutional neural network. This also challenges the basic concept of convolutional neural network and hence it helps in applying knowledge to a wider field.