Forecasting Cotton Whitefly Population Using Deep Learning
Keywords:Feature Extraction, Whitefly Population, Deep Learning, Forecasting Cotton
Agricultural is the primary source on which a country's economy depends. Pakistan is the world's fourth-largest cotton producer, making it one of its top cash crops. They can't figure out what sort of cotton is best for their climate because of government regulations, crop risks, and a low literacy rate among the general public and farmers. Using a model that considers temperature, this system attempts to describe the different types of plants. More than anything else, the pest's population is concerned with its diversity, how the weather affects its population, and when it is high or low. The primary purpose of this study was to develop a framework that can handle the complex process of the population of cotton white fly. Our main aim was to know the population of insects with their eggs and with their parents. And another purpose is to find out information about the variety with a low population of insects. To get early knowledge about the people against the temperature. So, cotton yield could be increased, and fewer chemicals should be used. Therefore, farmers' income could be improved. So, we will make the best model for predicting whiteflies on cotton ARIMAX. This model's accuracy was nearly on par with the statistical forecasting models. Because ARIMAX is a statistical model, it may also be used for forecasting.
How to Cite
This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License