Advanced AI technologies for optimizing Crop–weed interaction management: A data-driven modeling approach

Artificial intelligence has become the most widely used and trusted component of research in almost all disciplines of science and technology, starting from engineering, online businesses, and industry, to biotechnology and agriculture. Successful rice crops with maximum yield and weed management are the target set by several developed as well as developing countries, based on a combination of cultural and chemical control methods. In this paper, the weed control strategy through the competition model is documented with the aid of the time-series forecasting tool of artificial intelligence. A time-dependent computational framework is built based on the real data, and by utilizing supervised learning algorithm, incorporated with delay. It is emphasized during this research that the accurate precision of time delays in competition models can help in developing the weed control strategies more efficiently and can further support in implementing these strategies as precision models for other crop protection challenges.