Corn Yield Prediction Using Crop Growth and Machine Learning Models

Abstract

Undoubtedly, the advancement of IoT technology has created a plethora of new applications and a growing number of devices connected to the internet. Among these developments emerged the novel concept of smart farming. In this context, sensor nodes are used in farms to help farmers acquire a deeper insight into the environmental factors affecting their productivity.

In recent years, we have witnessed an emerging trend of scholarly literature focused on smart farming. Some focus has been on system architecture for monitoring purposes, while another area of interest includes yield prediction. Humidity, air and soil temperature, solar radiation, and wind speed are some key weather elements monitored in smart farms.

We introduce a mechanistic crop growth model to predict crop growth and subsequent yield, subject to weather, soil parameters, crop characteristics and management practices. We also seek to measure the influence of nitrogen on yield throughout the growing season. The machine learning models are trained to emulate the crop growth model in the state of Iowa (US). The multilayer perceptron (MLP) is chosen to evaluate the model prediction as it generates fewer errors.

Furthermore, the MLP optimization model is used to maximize corn yield. The experiment was performed using different scenarios, stochastic gradient descent (SGD), and adaptive moment estimation (Adam) optimizers. The experiment results revealed that the SGD optimizer and the dataset with the scenario of unchanged parameters provided the highest crop yield compared to the mechanistic crop growth model.