Crop Yield Prediction using Granular SVM
G. Sudha Sadasivam

Dr. G Sudha Sadasivam*, Department of CSE, PSG College of Technology, Coimbatore, India.

Manuscript received on February 20, 2021. | Revised Manuscript received on February 24, 2021. | Manuscript published on March 30, 2021. | PP: 85-98 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5417039621 | DOI: 10.35940/ijrte.F5417.039621
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Abstract: Agriculture is the backbone of the Indian economy. Farming is a major source of income for many people in developing countries. Prediction of yield of crops is desirable as it can predict the income and minimise losses for the farmersunder unfavorable conditions. But predicting crop yield is a challenging task in developing countries like India. Conventionally, crop yield prediction is done using farmer’s expertise. The sustainability and productivity of a crop growing area are dependent on suitable climatic, soil, and biological conditions. So, data mining techniques based on neural networks, Neuro-Fuzzy Inference Systems, Fuzzy Logic, SMO, and Multi Linear Regression can be used for prediction. Previous work has performed yield prediction based on crop models considering only some of the environmental factors. This work uses a Support Vector Machine (SVM) to predict the crop yield under different environmental conditions that include soil, climate, and biological factors. Applying granular computing enables dividing the problem space into a sequence of subtasks. So, the hyperplane construction of SVM can be parallelized by splitting the problem space. Testing can also be parallelized. The main advantage is that linear SVM can be used to handle higher dimension space. Time complexity is reduced. Prediction using granular SVM can be parallelized using appropriate techniques like MapReduce/GPGPU. IoT-based agriculture increases crop yield by accurate prediction, automation, remote monitoring, and reducing wastage of resources. IoT-based monitoring systems can be used by farmers, researchers, and government officials to analyze crop environments and statistical information to predict crop yield. This paper proposes an IoT-based system to predict crop yield based on climatic, soil, and biological factors using parallelized granular support vector machines. 
Keywords: Yield Prediction, SMO, Granular Support Vector Machines, Map Reduce, GPGPU, IoT, Automation, Remote monitoring.