AI-driven optimal control modeling for yield enhancement using nonlinear diffusion dynamics

This article develops an optimal management model based on artificial intelligence approaches to increase agricultural productivity. The model incorporates three state variables (plant density, nutrient resources, and yield) with their control signals in a 2D space, accounting for nonlinear diffusion processes. The dynamic system is described by differential equations, and an optimal control solution was obtained using Pontryagin's maximum principle. Simulations were conducted in Python environment, with results analyzed visually through graphs and 3D images. The research demonstrates effective application of digital transformation in agriculture and provides a theoretical foundation for designing sustainable agricultural technologies.

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