Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique

被引:0
|
作者
Kumar, Rakesh [1 ]
Singh, M. P. [1 ]
Kumar, Prabhat [1 ]
Singh, J. P. [1 ]
机构
[1] NIT Patna, Dept CSE, Patna, Bihar, India
关键词
Climate; RGF (Regularized Greedy Forest); Soil composition; CSM (Crop Selection Method); GBDT (Gradient Boosted Decision Tree); regularization; regression problem; NEURAL-NETWORK;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Agriculture planning plays a significant role in economic growth and food security of agro-based country. Selection of crop(s) is an important issue for agriculture planning. It depends on various parameters such as production rate, market price and government policies. Many researchers studied prediction of yield rate of crop, prediction of weather, soil classification and crop classification for agriculture planning using statistics methods or machine learning techniques. If there is more than one option to plant a crop at a time using limited land resource, then selection of crop is a puzzle. This paper proposed a method named Crop Selection Method (CSM) to solve crop selection problem, and maximize net yield rate of crop over season and subsequently achieves maximum economic growth of the country. The proposed method may improve net yield rate of crops.
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页码:138 / 145
页数:8
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