Convolutional Neural Network

Convolutional neural networks are similar to feed forward neural networks, where the neurons have learnable weights and biases. Its application has been in signal and image processing which takes over OpenCV in the field of computer vision.

Below is a representation of a ConvNet, in this neural network, the input features are taken in batch-wise like a filter. This will help the network to remember the images in parts and can compute the operations. These computations involve the conversion of the image from RGB or HSI scale to the Gray-scale. Once we have this, the changes in the pixel value will help to detect the edges and images can be classified into different categories.

ConvNet are applied in techniques like signal processing and image classification techniques. Computer vision techniques are dominated by convolutional neural networks because of their accuracy in image classification. The technique of image analysis and recognition, where the agriculture and weather features are extracted from the open-source satellites like LSAT to predict the future growth and yield of a particular land are being implemented.