Search for a command to run...
A novel idea called “smart farming” aims to increase the productivity and effectiveness of agriculture by utilizing cutting-edge information technologies. Using the most recent developments in automation, artificial intelligence, and networking, farmers are better able to keep an eye on every step of the process and apply exact treatments selected by machines with superhuman accuracy. With an expanding global population comes a rise in the need for both employment and food. The farmers' traditional practices fell short of meeting these demands. New automated methods were consequently proposed. These innovative techniques satisfied the world's food needs while also creating jobs for billions of people. For effective farm management, a variety of techniques are utilized, including IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Deep learning is one of these, and it is a developing field of study for crop yield forecasting. Engineers, data scientists, and farmers are still developing methods to optimize the amount of human labor needed in agriculture. As key information sources improve, smart farming transforms into a learning system that grows smarter every day. Artificial neural network principles are used in a machine learning method known as deep learning. The depth of deep learning networks, which also allows them to detect latent structures in unlabeled, unstructured data, is what sets them apart from neural networks. Compared to ML techniques, deep learning networks that automatically extract features without human involvement have a considerable advantage. In this research, we suggest a recommendation model and an algorithm to assess crop yield in the upcoming year. We have contrasted the deep learning algorithm with the random forest machine learning algorithm. These algorithms have been the subject of a brief comparison. Python has been used to implement the suggested model.