Application of fuzzy combined SVM & graph theory for agriculture productivity prediction

Abstract

A fuzzy integrated support vector machine and graph theory concepts are represents the data models for predicting a production. On this account, it has been used in various platforms such as agriculture, medicine, and various engineering applications. Therefore, the development of new computational development for predicting the productivity of events in terms of farming structure is very significant in agriculture. This method used fuzzy integrated support vector machine and graph theory to perform structural tasks suggested by crop influencing factors. Finally, the results obtained illustrate the advantage of predicting the rate of productivity, in addition to the importance of system recommendations that fail to produce the expected output volume at the time of setup or fail to produce the expected output quantum.