Crop Yield Prediction Using Remote Sensing and Meteorological Data
Avnika Shah, Rhea Agarwal, B. Baranidharan
Abstract
Proper agricultural planning is important in a vast country like India due to regular occurrence of floods, droughts, and extreme weather conditions. The farmers need to have prior knowledge regarding expected crop yield and crop condition in their specific area to make their financial and agricultural decisions accordingly. In the past any kind of agricultural assessment was based on manual survey and data collection, but this outdated approach has been made more precise with easy access to Remote Sensing Data. Remote Sensing has made distinguishing land cover and vegetation much easier with the assistance of Normalized Difference Vegetative Index (NDVI). This paper proposes a recommendation framework, which will not only predict the Crop Yield with the help of various Machine Learning and statistical algorithm like XGBoost, Gradient Boosting but also extract trends by evaluating various parameters such as NDVI Rainfall, Temperature, Air Quality Index (AQI) etc.