Generation of Site-specific Nitrogen Response Curves for Winter Wheat using Deep Learning

Nitrogen fertilizer response (N-response) curves are tools used to support farm management decisions. The conventional approach to model an N-response curve is to fit crop yield in response to a range of N fertilizer rates as a quadratic or exponential function. The purpose of the model is to identify the profit-maximizing N rate given the costs of nitrogen and the price paid for the crop yield. We show that N-response curves are not only field-specific but also site-specific and, as such, economic optimal (profit-maximizing) rates should be calculated for each field each year promoting the use of on-field precision experiments (OFPE) utilizing precision agriculture technologies. We propose a methodology that allows deriving N-response curves automatically instead of using parametric curve-fitting approaches. Thus, we obtain a specific non-parametric N-response curve for each 10 m x 10 m cell of a grid virtually draped on the field. First, we train a convolutional neural network called Hyper3DNetReg using remotely sensed data collected during the early stage of the winter wheat growing season (March) to predict crop harvest yield values. The neural network models the behavior of the field under different environmental and terrain conditions. Then, we use the trained prediction model to obtain an N-response curve per cell by simulating what would be the yield response given a range of nitrogen rate values between 0 and 150 pounds per acre (lbs/ac). Results show that the shape of the N-response curve depends on the region of the field from which it was calculated. Related work will address the problem of generating prescription maps that merge the site-specific economic optimal rates calculated from our N-response curves while also minimizing the overall fertilizer applied and the number of jumps between consecutive cells’ nitrogen rates.