Soil Forecasting and Classification Using JSO and Intelligent Technique With Big Data on Crop Yield

Accurate and rapid spatial classification of soil types and predicted production based on large spatial data has proven to be important factors for realistic purposes. In this regard, spatially clear information about the type of crop can be used constructively to assess the area for a variety of monitoring and decision-making applications, such as crop insurance, land leasing and supplies. Supply chain and financial market forecasts. The main impetus behind the current research is the effective description of the modified support vector machine (MSVM) for efficient classification of soil types. The forecast of the harvest and the expected yield depend entirely on the type of soil. In this paper, it is very important for an effective management of the company to have an adequate production forecast based on the combination of many factors that have a corresponding effect. The document performs three main functions, for example: Significant data reduction, soil classification and plant composition, including production forecasts. The harvest, in fact, varies from farm to farm depending on the date of planting, the variety, the soil and the organization of the harvest. Therefore, the category of soil to be used must be determined effectively. The document shows the big data inserted. The category of soil is determined by the method of shrinking the paper. Kernel principle component analysis (KPCA) in turn removes the maps.