Predicting inorganic nitrogen content in the soil using Machine Learning

This ground-breaking project addresses a critical issue in crop production: precisely determining plant-available inorganic nitrogen (IN) in soil to optimize fertilization strategies. Current methodologies frequently struggle with the complexities of determining a soil's nitrogen content, resorting to approximations and labor-intensive soil testing procedures that can lead to the pitfalls of under or over-fertilization, endangering agricultural productivity. Recognizing the scarcity of historical inorganic nitrogen (IN) data, this solution employs a novel approach that employs Generative Adversarial Networks (GANs) to generate statistically similar inorganic nitrogen (IN) data. 

This synthetic data set works in tandem with data from the Decision Support System for Agrotechnology Transfer (DSSAT). To address the data's inherent time-series nature, we use the power of Long Short-Term Memory (LSTM) neural networks in our predictive model. The resulting model is a sophisticated and accurate tool that can provide reliable estimates without extensive soil testing. This not only ensures precision in nutrient management but is also a cost-effective and dependable solution for crop production optimization.