Predictive Modeling of Crop Yield in Precision Agriculture Using Machine Learning Techniques

The purpose of this work is to investigate the operation of machine learning (ML) methods for prophetic modeling of crop yield in production agriculture. In the context of ultramodern agricultural techniques, perfection husbandry provides the implicit opportunity to maximize the utilization of resources, boost productivity, and reduce the negative impact on the environment. One of the most important aspects of perfect husbandry is the capacity to directly forecast crop yields, which makes it easier for growers and other stakeholders to make decisions based on accurate information. Using machine learning algorithms such as decision trees, arbitrary timbers, support vector machines, and neural networks, the purpose of this investigation is to develop predictive models that are capable of predicting crop yields based on a variety of input variables. These variables include soil characteristics, rainfall data, crop operation practices, and remote seeing imagery. This project aims to contribute to the improvement of ideal husbandry techniques by utilizing the power of machine learning-driven prophetic modeling. This will allow growers to make opinions based on facts, which will improve the sustainability and profitability of agriculture.