论文信息 - Crop Yield Prediction

Crop Yield Prediction

Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance for crop yield is compared with Random forest, Support Vector Machine, Weather, K Nearest neighbor. , which shows better performance results for each selected weather parameters. Crop yield rate depends upon various parameters such as the geography of area, soil type, soil nutrients, soil alkaline, weather condition, etc. The combination of these parameters can be used for selection of suitable crops for a farm or land to gain maximum yield. In this manuscript, soil and weather parameters such as soil type, soil fertility, maximum temperature, minimum temperature, rainfall are used to identify suitable crops for specified farm or land.

Pallavi Shankarrao Mahore | Dr. Aashish A. Bardekar | Aashish. A. Bardekar

[1] James M. Warner,et al. Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia , 2018, Climatic Change.

[2] K.V.S.N. Rama Rao,et al. Exploring the Impact of Optimal Clusters on Cluster Purity , 2018, 2018 3rd International Conference on Communication and Electronics Systems (ICCES).

[3] Alex J. Cannon,et al. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods , 2016 .

[4] Jennifer S. Raj,et al. RECURRENT NEURAL NETWORKS AND NONLINEAR PREDICTION IN SUPPORT VECTOR MACHINES , 2019, Journal of Soft Computing Paradigm.

[5] Srikanth Bethu. Data science: Identifying influencers in social networks , 2018 .

[6] Salah Sukkarieh,et al. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..

[7] Louis Kouadio,et al. Evaluation of the integrated Canadian crop yield forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape , 2015 .

[8] G. Karuna,et al. Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network , 2018, Periodicals of Engineering and Natural Sciences (PEN).

[9] GIS-MAP Based Spatial Analysis of Rainfall Data of Andhra Pradesh and Telangana States Using R , 2017 .