Implementation of Artificial Intelligence in Agriculture: A Review for CMS Optimization

Agriculture has a critical role to play in the financial domain. Likewise, automation of multiple processes in agriculture has been a great concern as well as an alarming subject across the world. The population all over the world is growing at a high rate and with this increment, demand for agriculture and its jobs is also growing exponentially. The usual techniques that were used by the farmers are not efficient enough to meet these requirements. Along these lines, new digital techniques are presented. These new strategies satisfy the proper management of agricultural products as well as services so that farmers can make the most of technology to increase their profit rates. AI in the agricultural landscape has initiated a revolutionary change. It has guarded the harvest yield from different declining factors such as environmental changes, over population, dynamic business demands, and food safety issues. By using artificial intelligence we can foster smart farming practices to limit the loss of farmers and give them high returns. Using artificial intelligence platforms, one can collect an enormous amount of information from government and public sites or real-time monitoring and collection of different information is likewise possible by utilizing IoT (Internet of Things) and afterward can be explored with precision to empower the farmers for resolving every one of the issues faced by farmers in the agriculture area. This research is conducted in order to help local farmers everywhere in the world to manage their agriculture practices all the more effectively. The strategy discussed in this paper is leveraging the model of waterfall methodology for planning and creating a system smart enough by performing a sequential cycle that starts with data collection, requirement analysis, plan, coding, and testing and finally implements that system as a whole. This system can also be used to foster ideas to manage normal issues in agriculture information systems, to improve the policy programs, the augmentation, and analysis practices, and to manage data on agriculture. Finally, conclusion about agricultural information systems are discussed and suggestions for additional development of agriculture data systems is presented.