ABSTRACT
If digitalization has an influence on farming and nutriment fabrication routines, then it also makes it feasible to practice expertise and trailblazing data administration techniques in the farming industry. Digital farming aims to address a variety of existing problems with resource management, climate protection, and food security by using data from agricultural assets. However, the agriculture sector is self-motivated and intricate; it necessitates erudite managing methods. It is believed that the application of digital tools would improve decision-making and optimization. A digital twin (DT) is a computer-generated image of a farm that has the potential to boost output and efficiency while cutting down on waste and energy use. The present state of DT concepts is examined in this research, along with other digital farming techniques and technology. It covers a broad range of agricultural fields, including drone technology, soil, farm equipment, irrigation, and automation in harvest processing. In agriculture, DT components for data collection, modelling (including big data and cognitive computing simulation, enquiry, and prediction), and transmission are being studied. The application of new technology, like artificial intelligence (AI), powerful analytic and optimal models, big data computing, and dimensional simulation, expands farm management's growth potential. Virtual models can forecast and solve unforeseen problems in the fields with the use of real-time and ongoing information on agricultural assets. The advancement of DT systems can track, register, and evaluate data in order to forecast and propose the right choice for digital farming management.
