Agricultural systems have been modeled and prediction of yields used in the space since the beginning of agriculture. Improvements in the crop science and better tools made the task much easier through the ages. From using the position of the sun to determining that certain weeds signify a good harvest to actually determining what factors precede observable phenomena the space has become so advanced such that we are able to build better prediction models and the promise of quantum computation that can model much more complex systems and interactions among the individual parameters within the system promise to make us predict yields of crops with much better accuracy than has ever been deemed feasible. With the technology that we have available now, we can apply properties of physical systems on classical computers such as mimicking chaos theory to add randomness to our predictions as that is the way nature works. That randomness is due to how initial conditions might potentially fluctuate and we would normally call it random because we are missing certain parameters that, if we collect, would greatly improve how we predict physical chaotic systems. The aim of this work is to explore how we can incorporate chaos in agricultural systems by making use of a hybrid approach to known systems like dense neural networks and more recent methods such as echo state networks.