Predicting In-Season Soil Mineral Nitrogen in Corn Production Using Deep Learning Model

One of the biggest challenges in nutrient management in corn (Zea mays) production is determining the amount of plant-available nitrogen (N) that will be supplied to the crop by the soil. Measuring a soil’s N-supplying power is quite difficult and approximations are often used in-lieu of intensive soil testing. This can lead to under/over-fertilization of crops, and in turn increased risk of crop N-deficiencies or environmental degradation. In this paper, we propose a deep learning algorithm to predict the inorganic-N content of the soil on a given day of the growing season. Since the historic data for inorganic nitrogen (IN) is scarce, deep learning has not yet been implemented in predicting fertilizer content. To overcome this hurdle, Generative Adversarial Network (GAN) is used to produce synthetic IN data and is trained using offline simulation data from the Decision Support System for Agrotechnology Transfer (DSSAT). Additionally, the time-series prediction problem is solved using long-short term memory (LSTM) neural networks. This model proves to be economical as it gives an estimate without the need for comprehensive soil testing, overcomes the issue of limited available data, and the accuracy makes it reliable for use.