Precision Agriculture Through Weather Forecasting
The main aim of this research is to achieve precision agriculture by selecting suitable data analytics techniques, such as autoregressive integrated moving average, linear regression, artificial neural networks, and decision tree, by trained to forecast weather behaviors such as rainfall, humidity, and temperature.
Abstract
Weather plays a significant role in both agriculture and farming. Accurate weather predictions allow farmers to make informed decisions and manage their resources effectively in terms of yield and costs. Precision agriculture is an approach that uses information technology to manage farming operations. The main aim of this research is to achieve precision agriculture by selecting suitable data analytics techniques, such as autoregressive integrated moving average, linear regression, artificial neural networks, and decision tree. A prediction model is trained to forecast weather behaviors such as rainfall, humidity, and temperature. The projected results are correlated to determine which weather factors have the most significant impact on the paddy yield while opening possibilities for farming decision support and insights into productivity.