Precision farming and smart weather forecasting with novel CNN for evaluation of historic cyclone data to deliver future algorithms over support vector machine.

There are a number of technologies that might help farmers increase yields and decrease losses, but two of the most important are precision farming and accurate weather forecasts. Despite this, these systems need precise, up-to-date information on a wide range of factors, including soil quality, weather forecasts, and crop health. When it comes to potential destruction, cyclones are among the most concerning, and they may also have a major effect on farming methods. We can get a deeper understanding of the correlation between cyclones and crop yields by building and testing a custom-made convolutional neural network (CNN) using historical cyclone data. Eventually, this data might be utilised to create more accurate weather forecasts and precision farming algorithms to benefit farmers. Using this data, we can develop these cutting-edge algorithms. To train and verify the proposed framework, we used an existing dataset that includes cyclone data from the past. Compared to state-of-the-art approaches, the suggested architecture outperformed them significantly in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]