Enhancing Crop Optimization in Organic Agriculture : Insights from a Polynomial Regression Model and the Grain Bot Innovation

This paper analyzes 30+ years of county data including soil, weather, and yield of corn provided by Seong Do Yun and Benjamin M Gramig. With the prevalent rise in organic agriculture, ruralities slowly suffer from less optimized crop growth. The paper depicts the use of a polynomial regression model upon multiple variables affecting corn yield to adequately predict future crop growth and production. Findings have resulted in a 93% testing rate, indicating that Machine Learning models are significantly effective upon predicting yield rates in various conditions. To address the growing demand for advanced crop optimization, the grAIn bot concept is introduced as a unique solution. The grAIn bot serves as a smart-edge device, functioning as a hub for precise agricultural innovation. By integrating IoT sensors, cloud computing, and machine learning algorithms, the grAIn bot enables real-time monitoring and advanced prediction capabilities for optimal crop management, including factors such as soil pH, plant diseases, and irrigation techniques. This innovative approach holds promise for revolutionizing crop optimization practices in the pursuit of sustainable and efficient organic agriculture