Crop Yield Prediction Techniques using Remote Sensing Data
Kuldeep Singh, Sunila1, Sanjeev Kumar2
1Kuldeep Singh, Department of CSE, GJUST, Hisar, India.
2Dr. Sunila, Department of CSE, GJUST, Hisar, India.
3Dr. Sanjeev Kumar, Department of CSE, GJUST, Hisar, India.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3683-3689 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6217029320/2020©BEIESP | DOI: 10.35940/ijeat.C6217.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crop yield prediction is an art of forecasting the yield of crop before harvesting. Prediction of crop yield will be very useful for the government to make food policies, market price, import and export policies and proper warehousing well in time. The socio-economical impact of crop loss due to any natural disaster i.e. flood, drought can be minimized and humanitarian food assistance can be planned. The paper present a literature survey of various stastical method, empirical models,artificial neural network and machine learning regression techniques which are used with the data provided by the satellites. Many models are developed and results calculated are compared with the benchmark models are also presented. Keywords: Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Support Vector Machine, Decision Tree, Neural Network.
Keywords: Semantic Trajectory, location prediction, spatial temporal, Reality Mining dataset, mobile phone, LBS.