Crop Yield Prediction using Granular SVM

Agriculture is the backbone of Indian economy. Farming a major source income for many people in developing countries. Prediction yield crops desirable as it can predict and minimise losses farmersunder unfavorable conditions. But predicting crop challenging task countries like India. Conventionally, prediction done using farmer’s expertise. The sustainability productivity growing area are dependent on suitable climatic, soil, biological So, data mining techniques based neural networks, Neuro-Fuzzy Inference Systems, Fuzzy Logic, SMO, Multi Linear Regression be used prediction. Previous work has performed models considering only some environmental factors. This uses Support Vector Machine (SVM) to under different conditions that include climate, Applying granular computing enables dividing problem space into sequence subtasks. hyperplane construction SVM parallelized by splitting space. Testing also parallelized. main advantage linear handle higher dimension Time complexity reduced. appropriate MapReduce/GPGPU. IoT-based agriculture increases accurate prediction, automation, remote monitoring, reducing wastage resources. monitoring systems farmers, researchers, government officials analyze environments statistical information yield. paper proposes an system factors support vector machines.