Deep Learning Methods for Fertilizer Forecasting: Review of State of The Art

Forecasting fertilizer usage is essential to maximizing agricultural output and guaranteeing environmentally friendly farming methods. With previously unheard-of accuracy, new techniques for estimating fertilizer requirements have been made possible by recent developments in deep learning. The most recent deep learning methods used in fertilizer forecasting are summarized in this paper. In order to identify the most popular algorithms, most pertinent features, evaluation matrices for validating results, difficulties, constraints, and potential areas for further study on the fertilizer prediction system, we conducted a thorough review in this work. In order to make more accurate and timely predictions using deep learning algorithms like Artificial Neural Networks, Long-Short Term Memory, and Convolution Neural Networks, which can be evaluated using Root mean square error (RMSE), [8] we looked into 19 papers about deep learning and crop prediction. Based on our analysis, fertilizer prediction involves a large dataset made up of data on soil parameters, and other observational parameters. Considering the field of study, potential advancements, and difficulties in developing a deep learning system for predicting fertilizer.