This paper presents an approach for approximate suboptimal control of nonlinear systems with constraints by neural network based rainfall observer for guiding crop growth in extensive agriculture. We propose a neural-network rainfall observer approximation by means of historical rainfall information. The goal is to obtain a close-loop operation with rainfall information, whose design is based on optimal control theory. Thus, the neurocontroller design proposed helps to drive the growth development of the cultivation as cost function and final state errors are minimized by physical constraints on the process variables. Therefore, it is possible to establish the control scheme and policy according to the criterion that generates the highest profit margin in the process. The contribution shows an optimal policy to guide the crop from an initial to a desired state. The estimates are consistent in a weak sense, and the question whether they are pointwise consistent is still open. Nevertheless, in order to assess the performance and practical tractability of the neurocontroller, real data and computational results are shown for soybean crop at Santa Francisca, Cordoba, Argentina.