Research On A Predictive Machine Learning Approach For Better Crop Recommendation Using Electric Power Plant Fly Ash Soil Analysis

As the world continues to rely heavily on coal-fired power plants,a lot of fly ash and bottom ash,both of which are considered significant solid wastes,are being generated.Fly ash can be useful in small doses,but annually,a lot of it is dumped on farms where it degrades the soil and contaminates the food.It has been suggested that the incorporation of fly ash into soil can improve its physical,chemical,and biological qualities while also increasing the availability of a wide variety of macro-and micronutrients to plant life.When coal is burnt at high temperatures,a byproduct known as fly ash is created.This ash is well recognized as a major worldwide solid waste problem.Fly ash is a byproduct of several thermal processes,especially the burning of coal,and is commonly seen in power plants.Fly ash’s potential as a useful resource in agriculture and other industries has been well-documented and well acknowledged.Large volumes of ash,in addition to carbon dioxide and other gases,are produced when coal is burned.In order to lessen waste,lower disposal costs,and create more valuable products,fly ash is being testing.Land that could have been used for farming was contaminated,and groundwater was tainted,due to the methods used to dispose of fly ash.The agronomic potential of fly ash is investigated in this work using machine learning and deep learning.In this study,we successfully designed and implemented machine learning models that predict high-yield,low-effort solutions.In other words,we utilized machine learning to create a prediction model that can advise farmers on the best crop to plant in fly ash soil,so saving them both time and money.Additionally,we applied an ensemble learning methodology to improve the accuracy of our suggested strategy and found that it was far more accurate than previous methods.This study explores the effectiveness of ensemble learning using the Bagging technique combined with the XGBoost classifier.The objective is to improve the accuracy of predictive models.Comparative analysis is conducted to evaluate the performance of the XGBoost classifier against previous models.Results indicate that the XGBoost classifier achieves an impressive accuracy rate of 99.63%,outperforming the previous models.These findings highlight the potential of ensemble learning and the XGBoost classifier for enhancing prediction accuracy in various domains.