Publication Type : Conference Paper
Publisher : Springer
Source : Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-33-4859-2_45 (2021)
Url : https://link.springer.com/chapter/10.1007/978-981-33-4859-2_45
Campus : Bengaluru
School : School of Computing
Verified : Yes
Year : 2021
Abstract : arming is the foundation of a country’s economy and it’s essential to meet the growing demand of the market. Therefore, it’s important to develop technologies that enhance the agricultural yield. The yield of a crop depends on various factors like climate, soil attributes, and so on, which forms a huge volume of data. Machine learning is one of the efficient technologies that helps in the identification of patterns and rules in large datasets. This paper proposed a method named Intelligent Crop Yield Prediction Method (ICYPM) to solve crop selection problem, and maximize net yield rate of crop. The authors have implemented and analyzed ICYPM with regression algorithms like SVM and RF. RMSE with 4 other parameters were used to compare the algorithms. In this study SVM fared the best as it efficiently predicted the climate and yield. This research has been carried out in collaboration with Indian Meteorological Department of India.
Cite this Research Publication : Hanuman, V., Pinnamaneni, K.V., Singh, T., "Best Fit Radial Kernel Support Vector Machine for Intelligent Crop Yield Prediction Method", Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-33-4859-2_45 (2021)