Extreme Learning Mechanism for Classification & Prediction of Soil Fertility index (Journal of Pharmaceutical Negative Results, 2022)
The agricultural sector is crucial to the economic development of our country. It was agriculture that paved the way for human civilization to develop. India is a predominantly agricultural nation, with a substantial portion of its GDP dependent on harvests. As a result, we can confidently assert that agriculture has the potential to serve as the economic bedrock of our nation. When making agricultural plans, crop selection is crucial. A variety of factors, including crop prices, production rates, and government regulations, will influence which crops are chosen. There needs to be a lot of tweaks in the agricultural sector for the Indian economy to improve. The Indian government implemented the Soil Health Certificate Program in each state to recommend fertilizers and agricultural rotations. It keeps track of information on a nationwide system that may be used to conduct soil surveys of varying types. A number of groups, both non-governmental and representative of local associations, help farmers keep an eye on soil quality. In order to determine the optimal fertilizer application rate for a given ground depending on its fertility & crop requirements, soil testing is an invaluable tool. Using machine learning methods that are readily applicable to the agricultural sector, we may make great strides forward. In addition to the substantial role played by improvements in farming machinery and technology, the importance played by relevant and accurate information on many subjects cannot be overstated. The purpose of this study is to use Machine Learning techniques to categorize the soil nutrient indices and pH levels in the Southeast laterite area in Andhra Pradesh and to predict these values using soil characteristics. Here, the test and analysis make use of the values reported in the soil test results for the relevant soil parameters. Although similar research is needed in other countries, this one focuses on agricultural data with the goal of improving the precision with which soil problem interpretation is achieved in Indian agriculture.