Convolutional neural networks CNNs (CNN) are a type of deep learning model that is especially well-suited for image classification tasks. They consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers. These layers work together to extract features such as edges and textures from an image and use them to classify the image into one of several predefined categories.

Smart farming is a new concept that combines the use of technology, data and analytics to increase efficiency in the agricultural sector. Crop pattern classification is an important tool in this process as it can help identify different crop types for better management and better yields. In this blog post, we will discuss how crop pattern classification helps us achieve smarter agriculture using remote sensing, deep learning and other technologies.

Super-resolution is a technique used to increase the resolution of an image, typically using deep learning algorithms. The goal is to produce high-resolution images from low-resolution inputs that can be useful in a variety of applications such as satellite imagery, medical imaging and more.

The use of remote sensing technologies has changed the way we monitor and manage farmland. Farmers and researchers now have access to information about crop growth and soil health that was previously difficult or impossible to obtain, thanks to the capacity to collect data from satellites, drones, and other aerial platforms. In this blog post, we will look at some of the most important remote sensing applications in agriculture.