Optimization of Crop Yield Prediction Through Linear Modelling and Deep Learning Techniques Used in Precision Agriculture

Abstract

The substantial advancements in computer science and engineering have sparked interest in precision agriculture and led to the development of more advanced instruments and methods for enhancing farming practices. This study focuses on the use of machine learning and mathematical models for fertilisation optimisation and yield prediction as part of a Precision Agriculture strategy. In particular, provides the outcomes of forecasting winter wheat production and protein content across four farms using the amounts of nitrogen fertiliser sprayed on the fields. To maximise net yields on the next crop, fertiliser treatments have to be prescribed based on these projections. In particular, contrast approaches based on neural networks (deep and shallow) and multiple regression (linear and non-linear). The greatest results are obtained by a deep neural network that incorporates spatial sampling and is based on the stacked autoencoder, according to the findings.